Download Atrial Fibrillation Detection

Contents
1 Introduction
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2 Medical Background
2.1 Anatomy of the Human Heart . . . . . . . . . . . . . .
2.2 Atrial fibrillation . . . . . . . . . . . . . . . . . . . . .
2.2.1 Electrical Activity in NSR and in AF . . . . .
2.2.2 Classification of AF . . . . . . . . . . . . . . .
2.3 ECG Signal . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Formation of the ECG Signal . . . . . . . . . .
2.3.2 ECG in Normal Sinus Rhythm and Fibrillatory
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Rhythm
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3 Methodology
3.1 Database . . . . . . . . . . . . . . . . . . . . . .
3.2 Noise Level Estimation . . . . . . . . . . . . . . .
3.3 The Pan-Tompkins algorithm for QRS detection
3.4 Feature Extraction . . . . . . . . . . . . . . . . .
3.4.1 Feature Selection . . . . . . . . . . . . . .
3.5 Classification . . . . . . . . . . . . . . . . . . . .
3.5.1 Bayes Decision Theory . . . . . . . . . . .
3.5.2 Artificial Neural Network . . . . . . . . .
3.5.3 K Nearest Neighbor Classifier . . . . . . .
3.6 Post-processing and Diagnostic Decision . . . . .
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4 QRST Cancellation
4.1 Straight Forward Averaging Algorithm for QRST Cancellation
4.2 Improved QRST Cancellation . . . . . . . . . . . . . . . . . . .
4.2.1 Digital Filters . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 QRS Clustering . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 Sub-clustering based analysis on RR-interval . . . . . .
4.2.4 Appropriate Templates and Subtraction . . . . . . . . .
4.2.5 Frequency Spectrum of AF . . . . . . . . . . . . . . . .
4.2.6 Fourier Transform and Power Spectrum . . . . . . . . .
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5 Results and Discussion
5.1 Results on Belt Database . . . . . . . .
5.2 Results on MIT Database . . . . . . . .
5.3 Discussion . . . . . . . . . . . . . . . . .
5.3.1 Limitation of QRST Cancellation
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Contents
5.4
5.5
5.6
5.7
The first and the beat window II . . . . . . . . . . . . . .
QRS clustering Methods . . . . . . . . . . . . . . . . . . .
Survey of QRST cancellation using appropriate templates
Results summary . . . . . . . . . . . . . . . . . . . . . . .
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6 Summary and Perspective
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A Abbreviations and Acronyms
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B COOKING BOOK FOR AF DETECTION TOOLBOX
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C The M-file structure of AF detection using MIT [1] and OWN
database
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References
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List of Figures
2.1
2.2
2.3
2.4
2.5
The cardiac conduction system . . . . . . . . . . . . . . . . . . . 4
Diagram of electrical activity in NSR and during atrial fibrillation 5
Representative human ECG waveform . . . . . . . . . . . . . . . 7
The generation of the ECG signal in the Einthoven limb leads. . 9
ECG in sinus rhythm and fibrillatory rhythm . . . . . . . . . . . 10
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
A example from the MIT-BIH AF database . . . . . . . . . . . .
ECG miniature monitor and traces . . . . . . . . . . . . . . . . .
Noise Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Block diagram of the Pan-Tompkins algorithm for QRS detection
FeatureExctraction . . . . . . . . . . . . . . . . . . . . . . . . . .
DecisionTree . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A neuron with a single scalar input and bias . . . . . . . . . . . .
layers of back propagation . . . . . . . . . . . . . . . . . . . . . .
An example of 5-Nearest Neighbor classifier . . . . . . . . . . . .
Post-processing classifier output using moving non-overlapping
window containing 6 beats in this example. . . . . . . . . . . . .
3.11 Example of receiver operating characteristic curve . . . . . . . .
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
Example of AF episode acquired by wearable belt system . . .
ECG and their respective remainder ECG are shown for an example of NSR and AF . . . . . . . . . . . . . . . . . . . . . . .
ECG Example, uniform template and its respective residual signal after subtraction . . . . . . . . . . . . . . . . . . . . . . . .
Flow chart of QT cancellation algorithm. . . . . . . . . . . . .
Frequency response of the 50 Hz low-pass filter . . . . . . . . .
Frequency response of the 50 Hz Notch filter . . . . . . . . . .
Noisy ECG signal and filtered ECG signal . . . . . . . . . . . .
Schematic representation of QRS clustering . . . . . . . . . . .
Result of QRS clustering for the ECG example in the Fig. 4.3 .
The appropriate templates superimposed on heart beats of each
cluster or subgroup for ECG example in Fig. 4.3 . . . . . . . .
Flow chart of computing appropriate templates . . . . . . . . .
An AF example, appropriate templates for QRST Cancellation
and its respective residual signal . . . . . . . . . . . . . . . . .
Remainder of the AF Example using the forward averaging algorithm and improved QRST Cancellation. . . . . . . . . . . .
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iv
List of Figures
4.14 An NSR example, template for QRST Cancellation and its respective residual signal . . . . . . . . . . . . . . . . . . . . . . . . 42
4.15 Definition of beat window . . . . . . . . . . . . . . . . . . . . . . 43
4.16 Frequency spectra from the residual signals in the AF and nonAF cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.1
5.2
5.3
5.4
5.5
5.6
5.7
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5.10
5.11
5.12
5.13
5.14
5.15
5.16
5.17
QT features averaged over AF and NSR records . . . . . . . . . .
Histogram of QTCan14 and QTCan11 . . . . . . . . . . . . . . .
ROC curve for combining features of QTCan14 and RR interval
Plot features of AF and NSR episode from record 04746 . . . . .
AF example with not apparent fibrillatory waves . . . . . . . . .
NSR example with chaotic atrial activity. . . . . . . . . . . . . .
Noisy ECG signal causes considerable error in QRST cancellation.
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K-mean clustering partitions the QRS complexes into three clusters
Frequency spectrum of the entire P wave . . . . . . . . . . . . . .
Decision curves comparison . . . . . . . . . . . . . . . . . . . . .
Performance comparision . . . . . . . . . . . . . . . . . . . . . .
QDC Density Estimation . . . . . . . . . . . . . . . . . . . . . .
Density function . . . . . . . . . . . . . . . . . . . . . . . . . . .
Decision curves comparison . . . . . . . . . . . . . . . . . . . . .
QDC Density Estimation . . . . . . . . . . . . . . . . . . . . . .
Density function . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Tables
2.1
Classification of AF
5.1
Features of QRST Cancellation of chronic AF patients for each
record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Features of QRST Cancellation of NSR patients for each record
Averaging features for AF and NSR records . . . . . . . . . . .
Results of AF detection using QRST Cancellation features on
wearable belt system. . . . . . . . . . . . . . . . . . . . . . . .
Results of AF detection using features of RR interval and QRST
Cancellation on wearable belt system. . . . . . . . . . . . . . .
The duration of annotated segment in minutes and the number
of heart beats for each MIT AF database . . . . . . . . . . . .
AF detection using feature of RR interval, QRST Cancellation
and combining the both features on MIT database. . . . . . . .
AF detection . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AF detection . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
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1 Introduction
Atrial fibrillation (AF) is a common arrhythmia with a prevalence of approximately 0.4-1% in the general population. Prevalence increases with age and it
is estimated to be present in 5% of those older than 65, and 10% of those older
than 70. It is associated with an increased risk of stroke and mortality, as well
as congestive heart failure and cardio-myopathy. AF gives rise to a significant
increase in mortality [2].
To help in the fight against AF disease, a system is developed for the continuous monitoring of health status based on non-invasive wearable sensors integrated in the Philips strap. The disease knowledge is enriched over time as the
system learns the patient’s behavior, for example, by monitoring and “remembering” the heartbeat during daily activities. Biometric signals, primarily ECG
(electrocardiogram), are collected in the hospital or at home and displayed on
a central station.
ECG signal, which is a graphical representation of the potential differences
measured between two points on body surface versus time, is produced by
activation front of cardiac depolarization and repolarization. ECG signals are
largely employed as a diagnostic tool in clinical practice in order to assess the
cardiac status of the patient. As one of the most important pieces of vital
information, the ECG signal plays an important role in the continuous patient
monitoring for the people, who suffer from chronic cardiovascular diseases. This
abnormal excitation propagation of AF patients results in morphology changes
in ECG. The ECG of AF patients is characterized by irregular RR intervals
caused by chaotic atrial depolarization waves penetrating the AV node in an
irregular manner. We can not see any consistent P wave due to chaotic atrial
activity, replaced by a fibrillatory wave, caused by random reentry wavelets.
These features enable automatic diagnosis of AF.
This novel continuous ECG monitoring support automatic analysis of ECG
signal on pocket PC, e.g., mobile platform. Algorithms of ECG processing are
thereby on demand under this circumstance. This study proposes a solution
for automatic detection of AF available for pocket PC. The AF detection uses
features extracted from the ECG, which reflect the electrophysiological changes
manifest in ECG signal during AF. The most descriptive features were selected,
and evaluated using various classifier on MIT-BIT atrial fibrillation database,
as well as on a database collected from atrial fibrillation patients using a Philips
one lead ECG strap. This thesis contain the following contents:
• Medical Background: Anatomy of human heart, AF pathology, formation of ECG signal are introduced to aid in comprehension of electrophysiological changes in the ECG of AF patients.
2
Introduction
• Methodology: This chapter describes all the components in the framework of AF detection: database, preprocessing, features extraction, postprocessing and evaluation.
• QRST cancellation: The vast majority of the AF detector is based on
ventricular irregularity, but the drawback is that rhythms other than AF
can also have irregular ventricular responses. Therefore, attempts have
been made to detect AF fibrillatory activity using QRST cancellation.
• Results and Discussion: This chapter describes the results of AF detection on the two databases, using diverse features and combined features
along with various classifiers. This chapter also discusses the drawbacks
of the QRST cancellation and the other possible approaches.
2 Medical Background
AF is the result of a fractionated atrial electrical activity mainly due to the
shortening of atrial refractory period, which allows multiple wavelets pass
through the atrial mass. AF can probably cause both molecular modifications
of electrophysiological activity, and structural, functional, which contribute to
disturbance of initiation and propagation of excitation pattern in atrial tissue.
In the case of normal sinus rhythm (NSR), the excitation propagation originates
from the sinoatrial node, from the right atrium to the left atrium in a uniform
pulse wave, after a 0.1 second delay in the AV node, the excitation along the His
bundle musculature to the both ventricles. However, in the case of AF, reentry wavelets occur instead of uniform excitation propagation. The excitation
spreads throughout the atrium in a random pattern. These chaotic atrial depolarization causes rapid atrial activity at 300 to 600 bpm. Fortunately, the AV
node does not permit all of the excitation to propagate to ventricles. Only 1 or
2 among every 3 atrial signals can pass the AV node due to Wenckebach effect.
However the ventricle contracts at a high rate of 110 to 180 bpm, depolarized
by a variant cycle length. To aid in comprehension of the methods for AF detection, this chapter introduces the medical background including anatomy of
human heart, AF pathology, formation of ECG, as well as the the characters of
the ECG signal which distinguish between atrial fibrillation and normal sinus
rhythm.
2.1
Anatomy of the Human Heart
The heart constitutes together with the blood vessels the cardiovascular
system, which has the task of transporting blood through the body. In this
system the heart acts as a cyclically working pump and as a blood reservoir.
From a macroscopic spatial view the mammalian heart is located inside
of the thorax and near to the lungs enclosed in the pericardium. Large blood
vessels are connected to the heart. It is subdivided by septa into two functionally
and anatomically similar structures: the right and left half, which represents
the division of the blood circulation system in two different parts. The right
half collects the deoxygenated blood from the body and pumps it to the lungs;
the left half receives the oxygenated blood from the lungs to deliver it to the
body.
A constriction subdivides each half of the organ into two muscular regions
enclosing a cavity. The upper region is called the atrium, the lower is the
ventricle. The heart therefore consists of four chambers: i.e. left and right
atria as well as left and right ventricles. The atria collect the incoming blood,
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Medical Background
Figure 2.1: The cardiac conduction system. The numbers in the brackets are
the conduction time of the excitation propagation in seconds [3].
which is transported to the ventricles. From there the blood is moved to supply
the body and the heart itself. The atria and ventricles are composed of walls
surrounding a cavity, which is normally filled with blood [4].
The excitation propagation in the human heart produces electrocardiography (ECG) and regulates the heart contraction. Figure 2.1 depicts the cardiac
conduction system. The sinoatrial (SA) node emits an impulse from the leading pacemaker site. The impulse spreads immediately into the atrial cardiomyocytes and is transmitted through the entire atrial muscle mass to the atrioventricular node (AV node) After a brief delay in AV node, during which the atria
can contract and fill the ventricles with blood, the impulse is conducted through
the His bundle via Tawara bundles and a subendocardial network (Purkinje’s
fibers). Finally both ventricles are activated from endocardium to epicardium.
2.2
Atrial fibrillation
Atrial Fibrillation (AF) is a common arrhythmia with a prevalence of approximately 0.4-0.1% in the general population. Prevalence increases with age
and is estimated to be present in 5% of those older than age 65, and 10% of
those older than 70. AF is associated with an increased risk of stroke and
mortality, as well as congestive heart failure and cardiomyopathy [5][6].
In normal individuals, a brief episode of AF may cause palpitations, chest
discomfort and light-headedness. Palpitations (a sensation of a rapid and irregular heart beat) are the most frequent symptoms in those patients with
paroxysmal AF. When AF is persistent or permanent, patients suffer more of-
2.2 Atrial fibrillation
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ten non-specific symptoms like poor effort tolerance, breathlessness on exertion,
and lack of energy. AF can, by itself, cause severe CHF (congestive heart failure) after several weeks to months. The loss of atrial contraction also leads to
the enlargement of atria, thus the stasis of blood in the atria, which promotes
clot formation and the occurrence of thromboemboli. AF is the single most
important cause of ischaemic stroke in people older than 75 [5].
2.2.1
Electrical Activity in NSR and in AF
Figure 2.2: Diagram of electrical activity in NSR (a) and during atrial fibrillation (b). Representative APs are shown from the SA node, atrial myocytes,
AV node and ventricles. The vertical line on each AP recording corresponds to
a common time reference. LA, left atrium; LV, left ventricle; RA, right atrium;
R V, right ventricle [5].
The heart is a large muscular pump that drives blood around the body (see
section 2.1). To achieve this effectively, the heart’s chambers must be precisely
controlled electrically. Figure 2.2 a) illustrates the normal regular activity in
the physiological heart. The normal heart beat is initiated in the SA node
at the normal sinus rhythm, then is conducted regularly throughout the atria,
causing them to contract. The contraction of the atria propels blood into the
ventricles. After about 0.1 s delay in the AV node, the excitation spreads rapidly
through Hisbundle and the connected branches to the ventricles and initiates
their contraction. As a consequence, the blood is pumped from the ventricles
to all of the organs around the body.
6
Medical Background
Figure 2.2 b) reflects the disturbed excitation propagation in the heart with
AF. When episodes of AF occur, instead of the regular initiation of the heart
beat in the SA node, there is no single place where the heart activates in the
atrium. The wavefronts of excitation spread throughout the atrium in a random pattern (re-entry wavelets: multiple wavefronts of depolarisation), finding
another small region of tissue to depolarize. The atria are constantly activated
in this chaotic pathway, until several of them are captured by the AV node and
propagate to the ventricles. Although the AV node filters most of these extra
atrial signals, the heart rate still reaches 110 to 180 bpm. There is no effective
contraction of the atrial muscle in this situation.
2.2.2
Classification of AF
It has been long recognized that an episode of AF may be self-terminating
or non-self-terminating. The terms chronic and paroxysmal have been used,
but sometimes this definition results in difficulties in effectiveness of treatments
and therapeutic strategies. It is important for clinicians to ascertain whether
an incident of AF is the very first episode, that is, the initial event; whether it
is symptomatic or not; and whether it is self-terminating or not. If the patient
has had two or more episodes, AF is said to be recurrent. AF can be classified
by (see table 2.1):
Terminology
Initial event
(first detected
episode)
Paroxysmal
Persistent
Permanent
Clinical features
Symptomatic
Asympotomatic (first detected)
Onset unknown (first detected)
Spontaneous termination < 7 days
and most often < 48 hours
Not self-terminating
Not terminated or
terminated but relapsed
Arrhythmia pattern
May not recur
Recurrent
Recurrent
Established
Table 2.1: Classification of AF [7].
Paroxysmal AF: Episodes of paroxysmal AF usually self-terminate within 48
hours and, by definition, in fewer than 7 days. The heart changes from SR
to AF episodes lasting from seconds to days. The patient may only have
1 episode a year or be in AF most of the time, but the essential feature
is that most episodes terminate spontaneously.
Persistent AF: When an espisode of AF has lasted longer than 7 days, AF
is designated as persistent. Persistent AF may be the first presentation
of the arrhythmia or may be preceded by recurrent episodes of paroxysmal AF. When AF is persistent, termination using electrical cardioversion
may be required, which is used to restore NSR. Cardioversion delivers the
electrical shock instantaneous to the human heart, resulting in a momen-
2.3 ECG Signal
7
tary depolarisation of most cardiac cells simultaneously. It allows the SA
node to resume the normal pacemaker activity.
Permanent AF: When AF has been present for some time and fails to terminate using cardioversion or is terminated but replaces within 24 hours, it
is said to be established or permanent [7].
Figure 2.3: Representative human ECG waveform, adapted from [8]
2.3
ECG Signal
Each individual heartbeat is comprised of a number of distinct cardiological
stages, which in turn give rise to a set of distinct features in the ECG waveform. These features represent either depolarization (electrical discharging) or
repolarization (electrical recharging) of the muscle cells in particular regions of
the heart. This activation sequence generates ECG measured by electrode on
the patients skin in specific position, e.g. left, right hand and left leg. This
electrode locations on the extremities produce three different leads: Einthoven
leads I, II and III. The activation front contributing to ECG signal is contained
in Fig. 2.4. Figure 2.3 shows a human ECG waveform and the associated features. The standard features of the ECG waveform are the P wave, the QRS
complex and the T wave. Additionally a small U wave (following the T wave)
is occasionally present.
The timing between the onset and offset of particular features of the ECG
(referred to as an interval ) is of great importance. The two most important
intervals in the ECG waveform are the QT interval and the PR interval. The
QT interval is defined as the time from the start of the QRS complex to the end
8
Medical Background
of the T wave, i.e. Tof f -Q, and corresponds to the total duration of electrical
activity (both depolarization and repolarization) in the ventricles. Similarly,
the PR interval is defined as the time from the start of the P wave to the start
of the QRS complex, i.e. Q-Pon and corresponds to the time from the onset of
atrial depolarization to the onset of ventricular depolarization. In normal sinus
rhythm [9][10][11],
• P-R interval 120-200 milliseconds (0.12 to 0.20 seconds)
• QRS interval under 120 milliseconds (0.12 seconds)
• Q-T interval under 380 milliseconds (0.38 seconds)
2.3.1
Formation of the ECG Signal
The cardiac cycle begins with the P wave (the start and end points of which
are referred to as Pon and Pof f ), which corresponds to the period of atrial
depolarization in the heart. After the electric activation of the heart has begun
at the sinus node, it spreads along the atrial walls. The resultant vector of the
atrial electric activity is illustrated with a thick arrow in Fig. 2.4 (a). The
projections of this resultant vector on each of the three Einthoven limb leads
is positive. After the depolarization has propagated over the atrial walls, it
reaches the AV node. The propagation through the AV junction is very slow
and involves negligible amount of tissue; it results in a delay in the progress
of activation. (This is a desirable pause which allows completion of ventricular
filling.)
The P wave is followed by the QRS complex, which is generally the most
recognisably feature of an ECG waveform, and corresponds to the period of
ventricular depolarization. Once activation has reached the ventricles, propagation proceeds along the Purkinje fibers to the inner walls of the ventricles.
The ventricular depolarization starts first from the left side of the interventricular septum, and therefore, the resultant dipole from this septal activation
points to the right. The figure 2.4 (a) right shows that this causes a negative
signal in leads I and II. In the next phase, depolarization waves occur on both
sides of the septum, and the resultant vector points to the apex. After a while
the depolarization front has propagated through the wall of the right ventricle.
Because the left ventricular wall is thicker, activation of the left ventricular free
wall continues even after depolarization of a large part of the right ventricle.
Because there are no compensating electric forces on the right, the resultant
vector reaches its maximum in this phase (see R peak in Fig. 2.4 (b)), and
it points leftward. The depolarization front continues propagation along the
left ventricular wall toward the back. Because its surface area now continuously decreases, the magnitude of the resultant vector also decreases until the
whole ventricular muscle is depolarized. The last to depolarize are basal regions of both left and right ventricles. Because there is no longer a propagating
activation front, there is no signal either in Fig. 2.4 (c).
Ventricular repolarization begins from the outer side of the ventricles and
the repolarization front ”propagates” inward (see Fig. 2.4 (d)). The inward
2.3 ECG Signal
9
(a)
(b)
(c)
(d)
Figure 2.4: The generation of the ECG signal in the Einthoven limb leads. (a)
The cardiac cycle begins with the P wave, which corresponds to the period of
atrial depolarization in the heart. (b) and (c) The QRS complex is caused by
ventricular depolarization. (d) The T wave represents the ventricular repolarization. [8].
10
Medical Background
spread of the repolarization front generates a positive signal, denoted as T
wave. Because of the diffuse form of the repolarization, the amplitude of the
signal is much smaller than that of the depolarization wave and it lasts longer
[8].
2.3.2
ECG in Normal Sinus Rhythm and Fibrillatory Rhythm
Figure 2.5: Top: ECG in sinus rhythm. Buttom: ECG in finrillatory rhythm,
adapted from [12][13]
Normal heart rhythm is termed as sinus rhythm (SR) or normal sinus
rhythm (NSR). The ECG in sinus rhythms (see upper Fig. 2.5) are characterized by a conducted P-wave with a P-R interval between 0.12 and 0.20
seconds. The QRS width should be 0.04 to 0.12 seconds and and a Q-T interval
of less the 0.40 seconds. The rate for a normal sinus rhythm is 60 to 100 beats
a minute. If the rate is below 60 beats a minute but the rest is the same it is
a Sinus Bradycardia. If the rate is between 100 to 150 beats a minute with the
same intervals it is a Sinus Tachycardia.
The AF on ECG in figure 2.5 below is indicated by the absence of consistent
P-waves, due to the chaotic atrial depolarization. Chaotic atrial depolarization
waves penetrate the AV node in an irregular manner, resulting in irregular
ventricular contractions. The QRS complexes have normal shape, due to normal
ventricular conduction. However the RR intervals vary from beat to beat [7].
In AF, the atria is excited rapidly and irregularly at a rate of 400 to 600 bpm.
2.3 ECG Signal
11
Fortunately, the AV node doesn’t permit all of the excitations to propagate
to the ventricles. Only 1 or 2 among every 3 atrial signals can pass AV node
(Wenckebach effect). However the vertricles contract at a high rate of 110 to
180 bpm in the absence of drug therapy [6]. The ventricular rate during AF
(the effective “heart rate”) is thus no longer under physiological control of the
SA node, instead is determined by interaction between the atrial rate and the
filtering function of the AV node.
3 Methodology
The approach for AF detection uses data collected the Philips strap and MITBIH atrial fibrillation (AF) database [14]. The ECG signals were pre-processed
by a R peak detection algorithm before the features were extracted. The feature extraction using beat-to-beat features were chosen to reflect the physiological changes that manifest in the ECG signal. Various classifiers with input
of features were applied for pattern recognition. Finally, the accuracy of the
classification decision were measured by a statistical analysis.
3.1
Database
The first evaluation database is the MIT-BIH arrhythmia database. This database was the first generally available set of standard test material for evaluation
of arrhythmia detectors, and has been used for that purpose as well as for basic
research into cardiac dynamics at about 500 sites worldwide [12]. The original
analog recordings were made at Boston’s Beth Israel Hospital (now the Beth
Israel Deaconess Medical Center) using ambulatory ECG recorders with a typical recording bandwidth of approximately 0.1 Hz to 40 Hz. The individual
recordings are each ten hours in duration, and include two channels of ECG
signals each sampled at 250 samples per second with 12-bit resolution over a
range of ±10 millivolts. Eighteen long-term ECG signals of channel one were
chosen from the MIT-BIH atrial fibrillation database, recordings of human subjects with paroxysmal AF. The remaining five recordings containing fewer AF
episodes were not adopted. The Fig. 3.1 shows a example from the MIT-BIT
AF database.
Another database was collected from chronic AF patients using a one lead
ECG strap. This Philips belt has been developed with three integrated dry
electrodes. The electrodes based on carbon-loaded rubber was integrated in
Figure 3.1: A example from the MIT-BIH Atrial Fibrillation Database Record
04015, Grid intervals: 0.2 seconds (horizontal) and 0.5 mV (vertical) [13].
14
Methodology
(a)
(b)
Figure 3.2: (a) ECG monitoring strap designed for convenience (b) ECG traces
in Sinus rhythm of a subject at rest, measured by the wearable belt on the chest
[14].
strap with miniaturized shielded cable. The strap was worn around the chest.
The representative P wave, QRS-complex, distinct R peak and T wave can be
seen in Fig. 3.2, only slight morphology variation compared to the standard
ECG-leads [14]. In total seventeen patients were measured in Aachen clinic,
Germany. Among them nine patients suffer from chronic AF, the rest are
healthy subjects. 9120 beats from fourteen recordings (three recordings were
not used due to its bad quality) in total duration of 130 minutes were used for
processing. The data comes from regular but short recordings. The patients
are resting during measurement and the noisy data are not taken into account.
3.2
Noise Level Estimation
To determine the noisy parts of signal which were to be excluded, the discrete
wavelet transform with mother Daubechies 4 wavelet was used. We consider
the following model of a discrete noisy signal [15]:
y(n) = f (n) + σe(n), n = 1 . . . N
(3.1)
The vector y represents a noisy signal and f is an unknown, deterministic
signal. We suppose that e is Gaussian white noise N (µ, σ) = N (0, 1).
Donoho and Johnstone [15] propose to use the ”universal threshold” estimation for estimating the noise σ.
√
δ=
2 ln N σ̄
(3.2)
where σ̄ is an estimation of the noise variance σ 2 given by
σ̄ = median(|C(1, k)|)/0.6745
(3.3)
3.3 The Pan-Tompkins algorithm for QRS detection
15
The first scale |C(1, k)| in the wavelet transform contains high frequencies,
usually characteristic of noise. Afterwards, the energy function of the first scale
is computed to amplify the noisy parts of the signal and the estimation of
”average” white noise variance is performed by taking the median value of the
wavelet coefficients at this finest scale (3.3) - see Fig. 3.3.
Figure 3.3: Noise detection. The parts with saturation noise and high frequency noise have been successfully detected.
3.3
The Pan-Tompkins algorithm for QRS
detection
Pan and Tompkins [16][17] proposed a real-time QRS detection algorithm based
on analysis of the slope, amplitude, and width of QRS complexes. The algorithm includes a series of filters and methods that perform low-pass, high pass,
derivative, squaring, integration, adaptive thresholds and search procedures.
Fig. 3.4 illustrates the steps of the algorithm in schematic form.
Figure 3.4: Block diagram of the Pan-Tompkins algorithm for QRS detection [17].
Bandpass-filter The bandpass filter reduced the influence of muscle noise,
power-line interference, baseline wander, and T wave interference. The desired
pass band to maximize the QRS energy is approximately 5-15 Hz.
16
Methodology
Derivative operator The derivative procedure suppresses the low frequency
components of P and T waves, and provides a large gain to high components
arising from high slopes of the QRS complexes.
Squaring The squaring operation makes the result positive and emphasizes
large large differences resulting from QRS complexes; the small differences arising from P and T waves are suppressed. The high- frequency components in
the signal related to the QRS complex are further enhanced.
Integration The output of the derivative based operation will exhibit multiple peaks within the duration of a single QRS complex. The Pan-Tompkins
algorithm performs smoothing of the output of the preceding operations through
a moving-window integration filter and produces transformed ECG.
Adaptive threshold Two set of thresholds are used to detect QRS complexes
for the transformed to improve the reliability compared to using onr threshold.
The thresholds continuously adapt to the current characteristics of ECG signals
since they are based upon the most-recent signal and noise peaks. If a peak
exceeds THRESHOLD I1 during the first step of analysis, it is classified as a
QRS peak. If the search-back technique (described in the next paragraph) is
used, the peak should be above THRESHOLD I2 to be called QRS. For irregular
heart rates, the first threshold of each set is reduced by half so as to increase
the detection sensitivity and avoid missing beats. To be identified as a QRS
complex, a peak must be recognized as such a complex in both the integration
and bandpass-filtered waveform.
Search-back procedure The Pan-Tompkins algorithm maintains two RRinterval averages: RR AVERAGE1 is the average of the most-recent beats, and
RR AVERAGE2 is the average of the most-recent beats having RR intervals
within the range specified by
RR LOW LIMIT = 92%RR AVERAGE2
RR HIGH LIMIT = 116%RRAVERAGE2
(3.4)
Whenever a QRS is not detected for a certain interval specified as
RR MISSED LIMIT = 166%RRAVERAGE2,
(3.5)
the QRS is taken to the peak between the established two thresholds.
3.4
Feature Extraction
There are three important feature groups used in detection of atrial fibrillation:
features using RR interval information, features using P-wave morphology, and
features using QRST cancellation. We consider a combination of features from
all these groups.
3.4 Feature Extraction
17
The features were extracted in a sliding window consisting of 30 beats,
rather than breaking the heart beats into separately blocks. Each time the
window was shifted in one heart beat (1 R-R interval) forward. In this way,
an attempt was made to label each beat individually, rather than in a group,
e.g, the first ECG block extending from the first to thirtieth heart beats formed
the 1st features array, and the second ECG block extending from the second to
thirty-first intervals formed the 2nd features array, etc. This technique results
in one-to-one correspondence between features and beats in the stream- see Fig.
3.5.
Figure 3.5: Features were calculated in moving window containing 30 beats.
1. An attractive approach for extraction of ventricular activity is to model
the R-R interval sequence as a three-state Markov process [18]. Each
interval is characterized as representative of one of the three states S, R,
L by classifying it as short, regular or long. Intervals were called short if
they did not exceed 85% of the mean interval, long if they exceeded 115%
of the mean, and regular otherwise. The mean interval is determined
recursively by the relation for all observed R-R intervals rr(i) which do
not exceed 1.5 seconds:
rrmean(i) = 0.75 ∗ rrmean(i − 1) + 0.25 ∗ rr(i)
(3.6)
Assume that R-R interval sequence
T = {t1 , t2 , . . . , tn }
(ti ∈ {S,R,L})
(3.7)
is controlled by a stationary first-order Markov process characterized by
the transition probability matrix
Pi,j,R = P (ti |tj , R)
(R ∈ {AF, other})
(3.8)
where AF and other denote AF and other rhythms of the databases respectively. This matrix gives the probability moving from state i to j.
18
Methodology
Further features apart from Moody’s matrix were calculated. In the time
domain, the following parameters were extracted: standard deviation of
the NN interval (SDNN), the standard deviation of the average NN interval (SDANN), the square root of the mean squared differences of successive
NN intervals (RMSSD), the number of interval differences of successive
NN intervals greater than 50ms (NN50). In the frequency domain, power
in very low (VLF [0-0.01Hz]), low (LF [0.01-0.15Hz]), high (HF [0.150.5Hz]) frequency range and ratio LF/HF were estimated. Furthermore,
the two following non-linear parameters were computed as well: approximate entropy, a measure of complexity [19], and dentrended fluctuation
analysis [20], a measure of long-term correlations.
2. The second feature group is a test for a presence of P wave. In normal
sinus rhythm, the P wave can be observed before QRS complex while
in case of AF, there is no P wave presented. The P wave detection is
done using template matching where correlation coefficient is used as a
dissimilarity measure between actual P wave and template. A threshold
had to be chosen (0.1) to allow acceptance of very similar beats. In this
way, each beat was labelled as beat with P wave present or P wave absent.
3. Finally, the last feature group are frequency and domain properties of
ECG remainder obtained after QRST cancellation. The frequency spectra of ventricular and atrial activity overlap. The remainder electrogram
is needed to cancel the ventricular component and isolate the atrial activity component of the signal. The remainder was calculated by averaging
method [21]. Fiducial points for ventricular complexes were marked using
a method based on the algorithm presented by Pan and Tompkins [22].
This involved calculating the first and second derivatives of the electrocardiogram, adding their absolute values together, and marking the maxima
as fiducial points. Basically, the average beat was aligned with the fiducial
points of all dominant beat windows and subtracted.The other features
derived from QRST cancellation will be discussed in chapter 4.
Figure 3.6: Example of feature selection using decision tree algorithm. To
demonstrate the selection process, the validation set on which the tree was built
was much smaller than the validation set for training and testing classifiers.
3.5 Classification
3.4.1
19
Feature Selection
In total we obtained 45 features. In order to reduce the dimension of the feature
space we applied the decision tree C4.5 algorithm using the WEKA package [23].
We retained the two most significant features by looking at the first levels of
the resulting decision tree. One simplified example of the decision tree process
is shown in Figure 3.6 where two features of the R-R interval analysis and the
P wavelet template matching were selected.
3.5
Classification
These features were fed to classifiers to categorize the ECG data into two classes:
patients with or without AF. The database was split randomly into a training
set (30%) and test set (70%). For each beat, the correct classification into
AF / non-AF was known as 1 for AF and 0 for non-AF. The classifier was
trained using the ECG signal from the training set, and evaluated on the test
set. Different classifiers were tested from a toolbox for pattern recognition
(PRTools4) to get highest specificity and sensitivity. In the first case the equal
covariances matrices for Bayes classifier were assumed, which results in a linear
discriminant function based on Bayes normal densities (LDC). In the
second case the covariance matrices are different for each category. The Bayes
classifier for normally distributed classes with unequal covariant matrices is
termed as quadratic classifier based on Bayes normal densities (QDC).
The third classifier is a back propagation (BP) neural network with one
hidden layer of 10 neuron units and one output neuron unit (10-ANN). The
fourth one is 3-nearest neighbor classifier (3-KNN). These classifiers are
described in the subsections below.
3.5.1
Bayes Decision Theory
Bayes decision theory is a fundamental statistical approach to the problem of
pattern classification. For two category classification (AF and non-AF), we let
ω1 and ω2 denote the two states to be classified with priori probability P (ω1)
and P (ω2 ), and suppose x is the feature value. The joint probability density
of ωj can be written in two ways: P (ωj, x) = P (ωj |x)P (x) = P (x|ωj )P (ωj).
Therefore the Bayes formula is given by
P (ωj |x) =
P (x|ωj )P (ωj )
P (x)
(3.9)
where in this case of two categories
P (x) =
2
X
P (x|ωj )P (ωj )
(3.10)
j=1
The posteriori probability P (ωj |x) is the probability of the state being ωj
given that feature value x is measured. P (x|ωj ) is density function of x given
by ωj . P (x) is unimportant as far as making a decision is concerned. It is
20
Methodology
basically just a scale factor that states how frequently we will actually measure
a pattern with feature value x. It only guarantee us that P (ω1 |x)+P (ω2 |x) = 1.
By eliminating this scale factor, we obtain the following completely equivalent
decision rule:
Decide ω1 if P (x|ω1 )P (ω1) > P (x|ω2 )P (ω2)
otherwise decide ω2 (3.11)
There are many ways to represent pattern classifiers. One of the most useful
is in terms of a set of discriminant functions, gi (x), i = 1, . . . , c. The classifier
is said to assign a feature vector x to class ωi if
gi (x) > gj (x)
for all j 6= i
(3.12)
For the maximum a posteriori rule (MAP), the associated discriminant
functions become
g˜i (x) = P (ωi |x) = P (x|ωi )P (ωi)
(3.13)
Since the logarithm is monotonically increasing, the classification is unchanged if we take natural logs.
gi (x) = log P (ωi |x) = log P (x|ωi ) + log P (ωi)
(3.14)
Let’s assume that the likelihood densities are Gaussian distribution.
1
1 x−µ
P (x|ωi ) = √
exp[−
2
σ
2πσ
2
]
(3.15)
The normal density is completely specified by two parameters: its mean µ
and variance σ 2 . The general multivariate normal density in d dimensions is
written as
X
1
1
P 1/2 exp − (x − µi )T ( )−1
i (x − µi )
d/2
2
(2π) | i |
P (x|ωi ) =
(3.16)
where x is a d-component column vector, µ is the d-component mean vector,
P
P
is the d-by-d covariance matrix. | | and −1 Eliminating constant term,
the MAP discriminant functions become
P
gi (x) = |
X
i
X
1
|1/2 exp − (x − µi )T ( )−1
i (x − µi ) P (ωi )
2
(3.17)
expressed in logarithm
X
X
1
1
gi = − (x − µi )T ( )−1
|) + log(P (ωi ))
i (x − µi ) − log(|
2
2
i
(3.18)
This know as Quadratic Discriminant Function. The quadratic term,
called as Mahalanobis Distance:
3.5 Classification
21
X
kx − yk2(P)−1 = (x − y)T (
i
)−1
i (x − y)
(3.19)
P
−1 can be thought ofPas a stretching factor on the space. Note for an
identity covariance matrix ( i = 1), the Mahalanobis distance becomes familiar
Euclidean distance.
When the features are statistically independent and each feature has the
same variance σ 2 . In this case gi can be rewritten as
(x − µi )T (x − µi )
+ log P (ωi )
2σ 2
1
= − 2 [xxT − 2µTi x + µTi µi ] + log P (ωi )
2σ
gi (x) = −
(3.20)
However, the quadratic term xT x is the same for all i, making it an ignorable additive constant. Thus, we obtain the equivalent linear discriminant
functions
gi (x) = wiT x + ωi0
(3.21)
where
wi =
1
µi
σ2
(3.22)
and
1 T
µ µi + log P (ωi )
(3.23)
2σ 2 i
In short, the Bayes classifier for normally distributed classes is quadratic,
whereas the Bayes classifier for normally distributed classes with equal covariance matrices is a linear classifier [24][25].
ωi0 = −
3.5.2
Artificial Neural Network
Artificial neural networks are computational systems, either hardware or software, which mimic the computational abilities of biological systems by using
large numbers of simple, interconnected artificial neurons. Artificial neurons
are simple emulation of biological neurons [26]. Fig. 3.7 shows a neuron unit
with a single input. The scalar input p is transmitted through a connection that
multiplies its strength by the scalar weight w to form the product wp, which is
argument of the transfer function f . The bias b is viewed as a threshold. If the
wp greater than the threshold, the output is 1, otherwise it is 0.
The back propagation (BP) network is the most widely used training algorithm, consisting at least three layers: an input layer, at least one intermediate
hidden layer, and an output layer (see Fig. 3.8). With BackProp networks,
learning occurs during a training phase in which each input pattern in a training set is applied to the input units and then propagated forward. The pattern
of activation arriving at the output layer is then compared with the correct
22
Methodology
Figure 3.7: A neuron with a single scalar input and bias [27]
(associated) output pattern to calculate an error signal. The error signal for
each such target output pattern is then backpropagated from the outputs to
the inputs in order to appropriately adjust the weights in each layer of the network. After a BackProp network has learned the correct classification for a set
of inputs, it can be tested on a second set of inputs to see how well it classifies
untrained patterns [28].
Figure 3.8: Back propagation network consists at least three layers: an input
layer, at least one intermediate hidden layer, and an output layer.
The simplest implementation of back propagation learning updates the network weights and biases in the direction in which the performance function
decreases most rapidly - the negative of the gradient. One iteration of this
algorithm can be written [29]
xk+1 = xk − αgk
(3.24)
where xk is a vector of current weights and biases, gk is the current gradient,
and α is the learning rate. Learning in a backpropagation network is in two
steps. First each pattern is presented to the network and propagated forward
to the output. Second, a method called gradient descent is used to minimize
the total error on the patterns in the training set. In gradient descent, weights
3.5 Classification
23
are changed in proportion to the negative of an error derivative with respect to
each weight [28]:
∆wji = −ε[δE/δwji ]
(3.25)
where wji is the weight connecting unit i to unit j, and ε is constant. Weights
move in the direction of steepest descent on the error surface defined by the
total error:
E=
1 XX
(tpj − opj )2
2 p j
(3.26)
where opj be the activation of output unit uj in response to patter p and tpj
is the target output value for unit uj . The gradient is computed by summing
the gradients calculated at each training example, and the weights and biases
are only updated after all training examples have been presented. In summary,
the BP network learns by example, when it is provided with a learning set
that consists of some input examples and the known-correct output for each
case. The computational cycle is repeated until the network learns the problem
“well enough”, that means overall error value drops below some pre-determined
threshold. However, this operation is unpredictable, since the network finds out
how to solve the problem by itself.
3.5.3
K Nearest Neighbor Classifier
The KNN classifier is a very intuitive method. The KNN requires an integer k,
a set of labelled examples and a measure of “closeness” calculated by a distance
function. For a given unlabelled example x, the algorithm finds the k “closest”
labelled examples in the training data set and assign the new point x to the class
that appears most frequently within the k-subset. In other words, a decision
is made by examining the labels on the k-nearest neighbors and taking a vote.
Expressing in mathematic formula, the discriminant functions are given by [24]:
ki
(3.27)
k
ki is the number of example, labelled as class ωi , and enclosed in a spherical
volume around unlabelled example x. k is the total number of examples inside
the spherical region. Fig. 3.9 illustrates an example of 5-Nearest Neighbor
classifier.
Advantage:
gi (x) =
• Analytically tractable, simple implementation
• Uses local information, which can yield highly adaptive behavior
• Lends itself very easily to parallel implementation
Disadvantage
• Large storage requirements
24
Methodology
Figure 3.9: The test point is labelled by a majority vote of these samples
enclosed in a spherical region. In the case k = 5, the test point is labelled as
red [30].
• computationally intensive recall
• Highly susceptible to the curse of dimensionality
3.6
Post-processing and Diagnostic Decision
The results of classifier on testing data are further post-processed as shown
in Fig 3.10. The number of AF beats detected in the sliding non-overlapping
window of 30 beats was counted. An interval is marked as an AF interval, if a
number of AF, exceeding a particular threshold, is detected in the ECG block.
This particular threshold, designated as AF number threshold was consequently used as decision variable for receiving operator curve (ROC) analysis.
The threshold is designed for each block containing 30 heart beats to smoothing
the signal from small flauctuation, rather than evaluating each heart beat.
The diagnostic accuracy can be expressed through sensitivity, specificity and
predictability in a certain study population. Let the prior probabilities P(A)
and P(N) represent the fractions of data with the AF and the fraction of data
without AF, respectively. Let T+ represent a positive test result (indication of
the presence of AF) and T− a negative result (indication of the absence of AF).
The following possibility arises.
• A true positive is the situation when the test is positive for a data
with AF. The true-positive fraction (TPF) or sensitivity s+ is given as
P(T+ |A) or
S+ =
number of TP decitions
TP
=
number of data with AF
TP+FN
(3.28)
The sensitivity of a test represents its capability to detect the presence of
AF.
3.6 Post-processing and Diagnostic Decision
25
Figure 3.10: Post-processing classifier output using moving non-overlapping
window containing 6 beats in this example. If the segment contains normal
sinus beats more than a threshold is classified as AF absent, otherwise as AF
present.
• A true negative(TN) represents the case when the test is negative for
a data without AF. A true negative fraction (TNF) or specificity S− is
given as P(T− |N) or
S− =
number of TN decitions
TN
=
number of data without AF
TN+FP
(3.29)
The specificity of a test represents its accuracy in identifying the absence
of AF.
• A false negative(FN) is said to occur when the test is negative for a
data with AF; that is, the test has missed the case P(T− |A).
• A false positive(FP) is defined as the case where the results of the test
is positive when the individual being tested not have AF. The probability
of this type of error or false alarm, known as the false-positive fraction
(FPF) is P(T+ |N).
The efficiency of a test may also be indicated by its predictive values. The
positive predictive value (Predictability) PPV of a test, defined as
TP
(3.30)
TP + FP
represents the percentage of the cases labelled as positive by the test that
are actually positive [31].
P P V = 100 ·
26
Methodology
Figure 3.11: Example of receiver operating characteristic curve
It is desired to have a diagnostic test that is both highly sensitive and highly
specific. An ROC (receiver operate curve) is a graph that plots FPF (1specificity), TPF (sensitivity) points obtained for a range of decision threshold
or cut points of the decision method. An example of ROC curve is illustrated in
Fig. 3.11. In this case, this decision variable is particular threshold of detected
AF beats contained in ECG block (AF number threshold), as mentioned above.
By tuning the threshold parameter in the window of 30 beats, the optimal
trade-off between sensitivity and specificity can be found. The optimal tradeoff between sensitivity and specificity is defined as the point which has minimal
distance to the point (0,1) on the ROC curve, see the point connected to (0,1)
in red line in the Fig. 3.11. Here we assume that sensitivity and specificity are
equal important. For example, this test has a sensitivity of 99.6% and specificity
of 98.7 %, as the threshold is set to be 27. By varying the decision threshold,
we get different decision fractions, i.e, choose to operate the sensitivity and
specificity at any point along the curve. The ROC curve is independent of the
prevalence of AF.
4 QRST Cancellation
4.1
Straight Forward Averaging Algorithm for
QRST Cancellation
AF is indicated by the absence of consistent P waves, due to the chaotic atrial
depolarization. The RR intervals vary in time. In AF, the atria are excited
rapidly and irregularly at a fibrillatory rate of 300 to 600 bpm caused by reentry wavelets (see section 2.2). Fig. 4.1 depicts ECG of a chronic AF patient
measured by Philips strap. To sum up, AF is characterized by
1. ventricular irregular rhythm
2. presence of atrial fibrillatory activity
Figure 4.1: Example of AF episode acquired by wearable belt system
The most important feature in detecting AF involves ventricular irregularity. A vast majority of the cases of AF do, in fact, have marked ventricular
irregularity, but the drawback of this criterion is that rhythms other than AF
can also have irregular ventricular responses. So many studies propose to detect
the fibrillatory activity in the surface electrogram. The challenge in detection
of fibrillatory waves is due to its chaotic nature and small amplitude in comparison to ventricular activity, buried in QRS complexes and T wave in some
28
QRST Cancellation
Figure 4.2: High-voltage lead ECG and their respective remainder ECG are
shown for an example of (top) NSR and (bottom) AF. The right single heart
beat is template for subtraction in each case [32].
cases. Sometimes, the amplitude of the fibirllatory waves is small enough to be
invisible to the ECG reader.
Janet Slocum [21] presented a method to cancel the ventricular activity from
ECG, and used the power spectrum of the atrial fibrillatory wave to detect AF.
A mean beat was generated by averaging over all beat windows aligned by
the fiducial points, which is defined as R peak location. For all rhythm, the
mean beat was aligned with the fiducial points of all the beats windows and
subtracted. Figure 4.2 shows a result of QRST cancellation. Observe that the
fluctuating waves in place of P waves, sometimes also T waves, have a mean
value close to baseline. The average beat subtraction approach uses the fact that
AF is uncoupled to ventricular activity and, therefore the average is subtracted
to produce a residual signal which contains the fibrillation waveforms, whereas
the ECG of NSR has a small remainder after subtraction.
The above QRST cancellation relies on the assumption that the average
beat can represent each individual beat accurately. However, QRS morphology
varies often dynamically, caused by respiration, premature ventricular beats,
fusion beats, etc. The signal may be polluted by a wide range of phenomena
including myopotential and electromagetic noise and several other acquisition
related events. Fig. 4.3(a) depicts an ECG example of 20 seconds, collected
from a chronic AF patient using Philips strap (see section 3.1). Fig. 4.3(b)
illustrates a template in red dashed line superimposed on the example of ECG
in blue solid line. Obviously this template, calculated by averaging all the heart
beats does not match the ECG data well. The poor fits are due to dissimilar
QRS complexes, i.e, high variation in QRS morphology and a different ST
interval. Fig. 4.3(c) is the residual signal after subtraction, ranged from -1635
4.1 Straight Forward Averaging Algorithm for QRST Cancellation
Figure 4.3: (a) An ECG example of chronic AF patient with the length of
20 s, measured by wearable ECG-monitoring. (b) The Template averaging all
the heart beats and superimposed on the original ECG segment. (c) Remainder
using the uniform template
29
30
QRST Cancellation
to 710 mV. This cancellation has a poor performance, since the amplitudes of
some residual QRS complexes are even greater than the amplitudes of original
ones, and residual T wave are clearly present in ECG signals. For this purpose,
improvements in the cancellation method are carried out in this study, which
are discussed below.
4.2
Improved QRST Cancellation
Figure 4.4: Flow chart of QRST cancellation algorithm.
Fig. 4.4 represents the main sequence of the improved QRST cancellation algorithm. The fundamental frequency of the residual signal of fibrillatory
baseline is well below the 4 – 10 Hz range. However, preliminary study revealed that harmonics arising from the QRST often produce more energy in
the relevant frequency spectrum range than much lower amplitude fibrillatory
activity [33]. Therefore, accurate assessment of frequency spectrum of atrial
component required selective attenuation of the QRST. In general, the QRST
complex is attenuated using a template matching and subtraction technique.
The improvement was focused on computing appropriate templates. Appropriate templates are specified as more than one templates, created for each
morphology. Nevertheless only one template is calculated if the signal is very
regular. Linear-phase, low and high pass filtering were used to reduce baseline
wander and suppress noise before performing QRST cancellation. A Notch filter
eliminated the power-line interference. Clustering and RR-interval based beat
classification was performed so that beats with different morphology and cycle
length were separated into different classes. Anomalous beats were rejected in
further analysis. A beat average was then computed for each of the classes.
Finally, the remainder was subjected to Fourier Transform and displayed as a
power spectrum. Each step will be discussed in great detail in the following
subsections.
4.2 Improved QRST Cancellation
4.2.1
31
Digital Filters
ECG are often polluted by many other noise of various origins. Noise includes
muscle noise, artifacts due to electrode motion, power-line interference, baseline
wander, and T waves with high-frequency characteristics similar to QRS complexes. The linear digital filters reduce the influence of these noise sources, and
thereby improve the signal-to-noise ratio. In this study recursive digital filters
are used, with only small integer multipliers meaning they are both simple to
program and fast in execution [34].
Low-pass filter This class of filters is designed based on the formula:
yn = xn − xn−m
(4.1)
where yn represents the the current (filtered) output sample value from the
filter, xn represents the current input sample, and xn−m represents the input
sample delivered to the filter m sampling periods previously. This time-domain
description of the filter is converted into a transfer function G(z), which is
derived as:
G(z) =
Y (z)
= (1 − z −m )
X(Z)
(4.2)
G(z) is the transfer function. X(z) and Y (z) are z transforms of input x(n)
and y(n), respectively. In this case, there are m zeros equally spaced around the
unit circle, each of which gives rise to a transmission zero in the corresponding
filter frequency response. Cancellation of one of the zeros by coincident gives
the low-pass characteristic. The addition of the pole causes G(z) to be modified
to
G(z) =
1 − z −m
Y (z)
=
−1
1−z
X(z)
(4.3)
So that the time-domain recurrence formula becomes:
yn = yn−1 + xn − xn−m
(4.4)
The filter is recursive, since each output depends upon a previous output
as well as inputs. The integer m can be adjusted to give the desired cutoff
frequency. Filters of this type have the advantage of having a pure linear phase
characteristic. The phase is said to have a linear phase response if its phase
response satisfies one of the following relationships [35]:
θ(ω) = β − αω
(4.5)
The cutoff slope and the attenuation my be greatly reduced by using higher
order zeros, and cancelling pole, instead of first order. The transfer function
becomes:
32
QRST Cancellation
G(z) =
(1 − z −m )n
(1 − z −n )n
(4.6)
The transfer function of second-order low-pass filter applied in this algorithm
is:
G(z) = [
1 − z −2 2
]
2(1 − z −1 )
(4.7)
with the cutoff frequency is 50 Hz at the sampling frequency of 250 Hz.
Frequency response of the filter is shown in Fig. 4.5.
Figure 4.5: Top: Magnitude response of the 50 Hz low-pass filter, Button:
Phase response of 50 Hz low-pass filter
High-pass filter This class of filter can be extended to the high-pass filter
which is designed to remove the base-line drift in the ECG signal. The design of
the high-pass is based on subtracting the output of a first-order low-pass filter
from an all-pass filter. The transfer function for such a high-pass filter is
G(z) =
1
− 128
+ z −64 − z −65 +
1 − z −1
1 128
128 z
(4.8)
The low cutoff frequency of this filter is 0,5 Hz at the sampling frequency
250 Hz. The components of 0,05 Hz is reduced in amplitude by about 50 dB.
Removal of power-line interference A well-known method capable of reducing power-line interference is the use of a notch filter characterize by a unit
gain at all frequencies except at notch frequency where gain is zero. The transfer
function of a second-order notch filter is given by [10]:
4.2 Improved QRST Cancellation
G(z) =
33
Y (z)
1 (1 + a2 ) − 2a1 z −1 + (1 + a2 )z −2
= ·
X(z)
2
1 − a1 z −1 + a2 z −2
(4.9)
The notch frequency ω0 and 3-dB rejection bandwidth related to the filter
coefficients a1 and a2 by the following:
a1 =
2cos(ω0 )
,
1 + tan( Ω2 )
a2 =
1 − tan( Ω2 )
1 + tan( Ω2 )
(4.10)
In application, if the sampling frequency, sinusoidal frequency (50 Hz in this
case) and notch band width are fs , fd and BW Hz, then
ω0 = 2π(
fs
),
fd
Ω = 2π(
BW
)
fs
(4.11)
The desired rejection bandwidth can be obtained by adjusting BW. The
magnitude and phase response of the 50 Hz Notch filter are plotted in Fig. 4.6.
Figure 4.6: Top: Magnitude response of the 50 Hz Notch filter, Button: Phase
response of 50 Hz Notch filter
Figure 4.7 compared the noisy ECG signal and the filtered signal after removal of ECG signal after removal of baseline wandering, low frequency noise
and power-line interference using the filters described above.
4.2.2
QRS Clustering
In stead of subtraction by an uniform template for the whole block, several templates are designed due to differences in even interpersonal ECG morphology.
For this purpose, non-supervised clustering of QRS morphologies is performed
on the filtered ECG signal. Clustering creates groups of objects, or clusters,
in such a way that the morphologies of objects in the same cluster are very
34
QRST Cancellation
(a)
(b)
Figure 4.7: (a) Noisy ECG segment (b) ECG signal after removal of baseline
wandering, low frequency noise and power-line interference.
4.2 Improved QRST Cancellation
35
similar and the morphologies of objects in different clusters are quite distinct.
The cluster analysis on a data set is performed as following procedures:
1. At first, the QRS complex is extracted from QRS onset to QRS offset,
which is detected by segmentation program. Each QRS complex is aligned
with fiducial point and stored in an array in equal length. Short QRS
complexes were padded with zero to make them with the required length.
2. A dissimilarity matrix stores distances that are available for all pair of
objects. This matrix is represented by an m × m (the number of total
heart beats in a segment) table. The subscripts of the distance matrix
is consistent with the index of R peak. Equ.(4.12) is the dissimilarity
matrix, where each element d(i, j) represents the difference or dissimilarity
between the objects i and j. The row and column represents the objects.

DM
0 d(1, 2) d(1, 3) . . .
d(1, m)

0
d(2,
3)
.
.
.
d(2, m)


.
.
..
..
..
=
.



0
. . . d(m − 1, m)
0








(4.12)
For all points x, y and z, a distance function must have following properties.
• Nonnegativity: D(x, y) ≥ 0
• Reflexivity: D(x, y) = 0 if and only if x = y
• Symmetry: D(x, y) = D(y, x)
• Triangle inequality: D(x, y) + D(y, z) ≥ D(x, z)
d(i, j) is very small when objects i and j are very similar to each other,
and becomes larger the more they differ. To calculate the dissimilarity
between the objects i and j the most popular distance measure is used
called Manhattan or city block distance distance between i and j is given
by:
d(i, j) =
b
X
|xik − xjk |
(4.13)
k=a
where a, b is the first and last index of QRS array, respectively.
3. The distance information generated in the Equ. (4.12) is used to determine proximity of objects to each other. The pair of QRS complexes is
denoted as similar when their distance is less than the predefined cut-off,
termed as similarity threshold. The similarity threshold values are empirically selected to assure a sufficient number of QRS complexes included
in the several initial clusters. Fig. 4.8 illustrates the way the algorithm
groups objects into clusters. Let’s treat the QRS as green circles in space
36
QRST Cancellation
Figure 4.8: Schematic representation of QRS clustering
(see Fig. 4.8 a). The algorithm searches the similar pair of QRS complexes
along each row, and clusters them at first level (see the circles enclosed
by blue ellipse in Fig. 4.8 b). These newly formed clusters are linked to
other objects to create bigger clusters at higher level until there are no
overlapping elements in these groups, presented as the contents enclosed
by purple ellipse.
4. The remaining objects, which can’t be grouped into any clusters, are
labelled as abnormal QRS complexes (see the isolated circles in Fig. 4.8 b).
Unfortunately, the number of labelled abnormal QRS complexes depends
upon threshold similarity. To assure a strong correlation between the
objects of the same cluster, a small initial similarity threshold is selected.
In some instances, most QRS complexes could be categorized as abnormal
because of the different scale, irregularity of ECG, subtle abnormalities
in shapes. Therefore a second patient specific threshold is used.
5. To verify the constructed cluster, a second threshold is set to control the
amount of rejected QRS complexes. The similarity threshold (1st threshold) increases and clustering is computed at each iteration, till the final
amount of rejected QRS is less than a second threshold, called as abnormal threshold. More circles are gathered together for the enlarged
blue ellipse in the Fig. 4.8. The abnormal threshold is specified for the
individual data. In progressive iterations, the similarity threshold is increased. In later iteration, If the increment of similarity threshold does
not change the clustering, the abnormal threshold is also increased. A
greater abnormal threshold is assigned to a longer ECG segment.
Each beat in the ECG block was classified as either a dominant or anomalous beat. Heart beats that contained QRS complexes of the most common
morphology and amplitude for the rhythm strip were defined as dominant
beats, in another word, the heart beats except for outlier beats derived from
QRS clustering. Premature ventricular beat, fusion beats or beats that saturated the amplifiers are defined as anomalous beats, in another word, the
outlier beats derived from QRS clustering. Fig. 4.9 a) plots the ensemble of
4.2 Improved QRST Cancellation
37
(a)
(b)
Figure 4.9: (a) The ensemble QRS complexes of the ECG example in the Fig.
4.9. (b)Result of QRS clustering for this ECG example.
38
QRST Cancellation
QRS complexes of the ECG example in the Fig. 4.3. Fig. 4.9 (b) depicts the
result of QRS clustering for this ECG example, containing 22 heart beats. The
algorithm above divides 22 heart beats into two clusters: cluster 1 in magenta
color and cluster 2 in green color, made up of 14 and 5 objects, respectively.
Three heart beats are labelled as abnormal, plotted in blue color.
(a)
(b)
(c)
(d)
Figure 4.10: The appropriate templates (red line) superimposed on heart beats
(blue line) of each cluster or subgroup for the ECG example in Fig. 4.3. The
QRS complexes of this example are grouped into clusters in the Fig. 4.9. (a)
and (b) Cluster 1 in Fig. 4.9 containing medium, long and short RR interval is
divided into 2 subgroups further for short and medium rhythm. (c) Cluster 2
made up of only medium heart beats were not divided further. (d) The template
and residual signal for anomalous heart beats are set to zero.
4.2.3
Sub-clustering based analysis on RR-interval
AF is always associated with irregular heart rhythm, meaning the R to R beats
can have very different lengths. Here we want to subtract an average full beat
template from the ECG signal. In order to accommodate for the different beat
lengths, we calculate three different templates of short, medium and long length
for every QRS cluster if there are sufficient numbers for the subgroups. Thereby,
each RR interval is characterized as one of three state by classifying it as short
(S), medium (M) and long (L). Intervals are called short if they do not exceed
4.2 Improved QRST Cancellation
39
85% of the mean interval, long if they exceeded 115% of the mean, and medium
otherwise. The running mean interval is given by [18].
rrmean(i) = 0.75 ∗ rrmean(i − 1) + 0.25 ∗ rr(i)
(4.14)
where i is the current heart beat index. Subsequently, each cluster is analyzed based on RR interval classification. The cluster that contains heart beats
of different RR classes is divided into subgroups. For instance, cluster 1 containing 9 medium, 1 long and 4 short RR interval is divided into 2 subgroups
for short and medium rhythm (see Fig. 4.10 (a) and (b)). The single long
heartbeat is merged with medium subgroup.
4.2.4
Appropriate Templates and Subtraction
Figure 4.11: Flow chart of computing appropriate templates for the example
in the Fig. ??.
Each heart beat is segmented by two kinds of beat windows. The heart beats
for each class are averaged to generate appropriate templates. Cluster 2 in Fig.
4.10 (c) includes only medium heart beats, thus one template is calculated for
this class. In this way, appropriate templates are computed for similar QRS
morphology and RR interval. The template and residual signals for anomalous
beats in Fig. 4.10 (d) are set to zero to prevent their large remainders from
contributing to evaluation. Fig. 4.10 plots the computed template (red line)
superimposed on heart beats (blue line) of each cluster or subgroup for ECG
example in Fig. 4.3. Fig. 4.11 illustrates the steps of computing the appropriate
templates for this example in schematic form.
40
QRST Cancellation
Figure 4.12: (a) An ECG example of chronic AF patient with the length of
20 s, measured by wearable ECG-monitoring. (b) Appropriate Templates (red
line) superimposed on the original signal (blue line) (c) Remainder after QRST
cancellation using appropriate templates.
4.2 Improved QRST Cancellation
Figure 4.13: (a) Remainder of the AF Example using the forward averaging
algorithm with a uniform template. (b) Remainder of the AF Example using
the improved QRST Cancellation with several appropriate templates.
41
42
QRST Cancellation
Figure 4.14: (a) An ECG example of NSR patient with the length of 20 s, measured by wearable ECG-monitoring. (b) The template (red line) superimposed
on the original signal (blue line) (c) Remainder after QRST cancellation.
4.2 Improved QRST Cancellation
43
(a)
(b)
Figure 4.15: (a) the beat window I is defined as interval from Qon (i) to Qon (i+
1), (b) Beat window II is defined as interval from P R+Qon (i) to P R+Qon (i+1).
Two different definitions of beat window are established. Beat window I
is specified as interval form current QRS onset to the subsequent QRS onset
(from Qon (i) to Qon (i + 1)), see Fig. 4.15 (a). Beat window II is specified
as interval from the onset of the current P wave Pon (i) to the onset of the
subsequent P wave Pon (i + 1), see Fig. 4.15 (b). The boundaries of the beat
window distinguish between the beat window I and II.
Afterward, the corresponding templates are subtracted from the original
sequence after alignment at the fiducial point of each QRS complex. The template is padded with zero so that its length is equal to the longest heart beat
in the same class. Fig. 4.12 (b) depicts the template in red color superimposed
on original ECG data in blue color. The three blue heart beats observed in Fig.
4.12(b) are abnormal heart beats rejected in averaging, whose template and
remainder are set to zero. Fig. 4.12 (c) illustrates the residual signal, ranged
from -279 mV to 349 mV. Fig. 4.13 compared the residual signal using the
44
QRST Cancellation
QRST cancellation algorithm described in section 4.1 (see Fig. 4.13 (a)) and
improved QRST cancellation algorithm (see Fig. 4.13 (b)). This comparison
shows that, the improved algorithm performs considerably better than the forward averaging algorithm in the section StraightForwardAveraging. The QRS
remainder reduced significantly due to clustering and appropriate templates.
The minor residual signal for T wave was achieved by consideration of variation
in heart rate related ST interval.
In comparison to the AF example in the previous figures, Fig. 4.14 (a)
shows an ECG example of a patient in normal sinus rhythm with the length of
20 s, measured by wearable ECG-monitoring. The averaging template in red
color is superimposed on the original signal in blue color in Fig. 4.14 (b). The
remainder contains very small pieces of QRS complexes and noise in Fig. 4.14
(c)
4.2.5
Frequency Spectrum of AF
The fibrillatory wave is usually the result of multiple simultaneous reentrant
activation wavefronts. The average size of reentrant pathways during AF is
dependent on atrial wavelength, defined as the product of conduction velocity
and refractory period. Long wavelengths are associated with larger and fewer
wavefronts, occurring in paroxysmal AF patients, whereas short wavelengths
results in a great number of small circuits, occurring in chronic AF patients.
The vast majority of ECG recordings analyzed in previous investigations
have exhibited single-narrow banded frequency spectra when applying the
QRST cancellation [33][36][37][38][39][21]. Spectral decomposition of residual
ECG signal revealed that the dominant frequency may result from repetitive
activations by a reentrant source such as a single focal source within pulmonary
vein. The lower frequency components may represent various degree of spatiotemporal organization of waves propagating from that source to the rest of
the atria [40].
Fibrillatory frequency in human AF determined from the surface ECG exhibits a marked individual variability ranging between 4 and 10 Hz or 5 and
10 Hz [33][36][37][38][39][21][41], which is in agreement to the values obtained
from intra-atrial recordings in previous investigations [42] and physiological investigation: fibrillatory wave typically have a rate between 300 and 600 bpm
[43][21][44][39][6]. Asano et al induced AF with rapid pacing in 30 patients
undergoing an electrophysiological study [42]. Those patients where AF terminated spontaneously had an average fibrillatory frequency of 5.7 Hz, significantly lower than the 6.4 Hz recorded in the group of patients where the
arrhythmia persisted. Estimating fibrillatory frequency from the surface ECG,
Bollmann et al illustrated the fibrillatory frequency increases from paroxysmal
(mean 5.1 Hz) to chronic AF (mean 6.9 Hz). Chronic atrial fibrillation had
the highest frequency. Low frequency fibrillation is more likely to terminate
spontaneously or to respond to antiarrhythmic therapy, while high frequency
fibrillation is more often persistent and drug refractory [36].
4.2 Improved QRST Cancellation
4.2.6
45
Fourier Transform and Power Spectrum
The Fourier Transform decomposes a signal into purely harmonic wave, which
is given by [45]:
Z
∞
X(f ) =
x(t)e−j2πf t df
(4.15)
−∞
This transform breaks down a signal into its frequency-spectrum as a set
of sinusoidal components, converting it from the time domain to the frequency
domain. In practice the Fourier components of data are obtained by digital
computation rather than by analogue processing. It is not possible to apply
the Fourier transform in the Equ. 4.15 because it is defined for continuous
data. However, a Discrete Fourier Transform (DFT) is available for discrete
data. Assume that a waveform has been sampled at regular time intervals
T to produce the sample sequence x(nT ) = x(0), x(T ), . . . , x[(N − 1)T ] of N
sample values, where n is the sample number from n = 0 to n = N − 1. The
DFT of x(nT ) is then defined as the sequence of complex values X(kΩ) =
X(0), X(Ω), . . . , X[(N − 1)Ω] in the frequency given by Ω = 2π/N T . The N
real data values (in the time domain) transform to N complex DFT values (in
the frequency domain) is given by:
−1 −jkΩnT
N −1 −j2πnk/N
X(k) = ΣN
= Σn=0
e
,
n=0 e
with k = 0, . . . , N − 1 (4.16)
However, a large number of multiplication and additions are required for the
calculation of the DFT. For an N-point DFT, there will be N2 and N-12 of them
respectively. If N = 1024, approximately one million complex multiplication
and one million complex additions are required. Clearly some means of reducing these numbers is desirable. The fast Fourier transform (FFT) is a discrete
Fourier transform algorithm which reduces the number of computations needed
for N points from 2N 2 to 2N lgN , where lg is the base 2 logarithm using symmetric property. The computational redundancy in DFT is reduced applying
the periodic inherence of e−j2π/N [46]. 1024-points Fast Fourier Transform was
used to calculate the power spectrum in 0- f2s Hz frequency band, where fs is
sampling frequency. The ith element of the power spectrum (PS) is computed
from Xi of Fourier Transform X as [45]:
P S = (Xj Xj∗ )/1024
(4.17)
Fig. 4.16 depicts the frequency analysis of the residual signals from different
records of AF patients in the left panel (Fig. 4.16 (a), (c) (e)) and this of NSR
patients in the right panel: (4.16 (b), (d), (f)). Among them Fig. 4.16 (e)
and 4.16 (f) show the power spectra of the remainder from Fig. 4.12 (c) and
Fig. 4.14 (c). These six records are collected by Philips strap from independent
subjects (see section 3.1). Observe that in the AF case, the absolute power
are concentrated in the frequency band 4 – 10 Hz, either exhibiting a narrow
peak or great power component in this frequency band, whereas the residual
signal has a lower frequency component in this frequency band. Noise enhances
46
QRST Cancellation
(a) AF
(b) Non-AF
(c) AF
(d) Non-AF
(e) AF
(f) Non-AF
Figure 4.16: (a), (c) and (e): Frequency spectra from the residual signals are
estimated using FFT in the AF cases. (b), (d) and (f): Frequency spectra from
the residual signals in the non-AF cases.
the power components over the frequency band 0 – 4 Hz in the Fig. 4.16 (b)
and (d). The residual QRS signals in the remainder contribute to high power
content in the Fig. 4.16 (f).
The PS in the frequency band extending from 4 to 10 Hz and from 5 to
10 Hz are analyzed, since the start frequency of the band is still ambiguous
(see section 4.2.6). Two feature groups are extracted from the power spectrum:
mean values of dominant heart beats (see subsection 4.2.2) in the ECG block
(QTCan1 – QTCan10) and the evaluation of whole ECG block (QTCan11 –
QTCan16), including 30 heart beats. PR10
5 is the percentage of the absolute
power in the
frequency
band
5
–
10
Hz
to
the total power from 0 – 125 Hz
P
10Hz
=5
(P R510 = Pf125
f =0
P
P
) in the ECG remainder, and PR10
4 is related with power
ratio of frequency band 4 – 10 Hz
(P R410
P10Hz
P
=4
= Pf125
). Peak ratio denotes the
f =0
P
ratio of the numbers of the heart beats exhibiting maximal power in the 4 –
10 Hz frequency band to the amount of the dominant beats in the ECG block.
4.2 Improved QRST Cancellation
47
The list enumerates the features extracted from the PS of remainder.
1. QTCan1: The mean PR10
5 using the the beat window I (see Fig. 4.15
(a)).
2. QTCan2: The mean PR10
4 using the the beat window I (see Fig. 4.15
(b)).
3. QTCan3: Peak Ratio using the beat window I.
4. QTCan4: The mean PR10
5 using the beat window II.
5. QTCan5: The mean PR10
4 using the beat window II.
6. QTCan6: Peak Ratio using the beat window II.
7. QTCan7: The sum of QTCan1 and QTCan3.
8. QTCan8: The sum of QTCan2 and QTCan3.
9. QTCan9: The sum of QTCan4 and QTCan6.
10. QTCan10: The sum of QTCan5 and QTCan6.
11. QTCan11: PR10
5 using the beat window I.
12. QTCan12: PR10
4 using the beat window I.
13. QTCan13: Peak frequency in the PS using the beat window I.
14. QTCan14: PR10
5 using the beat window II.
15. QTCan15: PR10
4 using the beat window II.
16. QTCan16: Peak frequency in the PS using the beat window II.
5 Results and Discussion
The most features using the QRST cancellation algorithm noted in chapter 4
were fed into various classifiers, which categorize the ECG data into two classes:
AF or non-AF. This chapter will discuss the results using diverse features and
several classifiers on both databases: belt data acquired by Philips strap and
MIT-BIH AF database. The features are selected by statistical methods and
visualized. This chapter covers also the drawbacks of QRST cancellation algorithm. Possible approaches for QRS clustering and QRST cancellation are
introduced as well as their performances.
5.1
Results on Belt Database
At first the validation of the QRST cancellation algorithm has been done using
the database acquired by the Philips strap. A total of 6930 beats from 14
records in duration of 130 minutes were used for processing, including 8 records
of chronic AF patients (4650 heart beats) and 6 records from patients in normal
sinus rhythm (2280 heart beats). 16 features were extracted for each heart beat
based on QRST cancellation algorithm, defined in section 4.2.6. The table 5.1
shows the mean value of the features for each AF record, as well as the number
of heart beats, whereas the table 5.2 shows the similar analysis on 6 records in
normal sinus rhythm.
In general, the energy and spectral peak of remainders are concentrated
on frequency band 4 – 10 or 5 – 10 Hz in the case of chronic AF patients,
extracted by QT1 – QT6 and QT11 – QT16. However the remainders of NSR
have a lower frequency content in this area. These results are in agreement
with the statement mentioned in section 4.2.5. QT7 – QT10 combined peak
and power percentage features should be greater in the case of AF than NSR.
Higher QT3, QT6, QT13 and QT16 indicated that peak frequency between 4
and 10 Hz occurs more frequently in the AF records than the NSR records.
We expect the remainder exhibiting a spectral peak in 4-10 frequency band. In
fact, this is not always the case. The shift in the peak frequency of AF ECG
for some instances is due to noise, insufficient subtraction, etc. The reason will
be discussed in the section 5.3.1. Bollmann et al [33] didn’t detect the the peak
from 4 to 10 Hz for each data, either. The features QT7 – QT10 displayed a
wide range of values over the different patient.
The 16 features are averaged over the whole AF and NSR records, shown in
table 5.3. These results of AF and NSR are also plotted in Fig. 5.1, expressed
in purple and red bar respectively. As expected, all the mean values of the 16
features are much greater in the AF data than in the NSR case data, consistent
50
Results and Discussion
Table 5.1: Features of QRST Cancellation for each AF record. HB-Heart beats.
QT1
QT2
QT3
QT4
QT5
QT6
QT7
QT8
QT9
QT10
QT11
QT12
QT13
QT14
QT15
QT16
HB
AF(1)
55.44
63.90
81.21
55.73
63.87
81.27
136.65
145.11
147.01
145.14
46.92
52.17
6.06
47.83
52.03
6.18
540
AF(2)
45.06
53.48
57.56
43.80
51.92
54.41
102.62
111.04
98.22
106.33
32.34
39.09
4.80
31.76
37.88
4.82
690
AF(3)
39.06
47.58
41.51
39.02
46.44
38.29
80.57
89.09
77.32
84.73
32.36
38.29
4.93
33.34
39.34
4.31
900
AF(4)
35.31
40.05
31.42
37.16
40.24
30.07
60.73
68.02
50.23
61.31
29.28
32.49
3.55
30.58
36.67
4.04
330
AF(5)
47.38
55.19
60.28
46.84
54.46
59.59
107.67
115.67
106.43
114.05
38.97
43.85
5.08
38.91
43.67
5.09
1470
AF(6)
23.12
29.53
23.44
22.20
28.20
23.17
36.56
42.97
35.37
41.37
16.63
22.78
3.29
16.07
21.36
3.02
120
AF(7)
29.24
36.57
22.33
32.39
40.25
28.58
51.57
58.91
60.97
68.83
24.46
32.03
3.09
25.70
33.40
3.10
240
AF(8)
38.35
46.05
44.06
35.60
42.91
37.36
82.41
90.12
72.95
80.27
26.37
32.29
3.60
24.87
30.71
3.20
360
Table 5.2: Features of QRST Cancellation for each NSR record. HB-Heart beats.
QT1
QT2
QT3
QT4
QT5
QT6
QT7
QT8
QT9
QT10
QT11
QT12
QT13
QT14
QT15
QT16
HB
NSR(1)
36.20
42.05
39.71
16,67
19.16
5.77
75.90
81.76
22.44
24.93
25.36
29.16
7.49
14.84
16.46
10.78
300
NSR(2)
16.21
21.89
2.82
14.39
20.19
1.79
19.03
24.71
16.19
21.99
12.48
17.16
1.87
10.96
15.42
1.94
690
NSR(3)
21.32
30.32
31.26
17.83
24.03
7.29
37.58
43.58
25.12
31.33
19.19
24.13
2.39
13.01
17.75
2.18
480
NSR(4)
44.81
52.38
54.93
44.64
52.93
52.18
99.74
107.31
96.82
105.11
36.10
41.89
3.83
32.21
44.23
3.79
210
NSR(5)
33.89
41.81
17.92
32.38
40.07
15.15
51.81
59.73
47.53
55.23
30.65
38.72
2.82
30.30
38.05
2.69
300
NSR(6)
27.37
34.8
16.14
26.63
33.36
15.09
43.52
50.98
41.73
48.46
20.86
27.40
2.28
20.55
27.06
2.31
300
5.1 Results on Belt Database
51
with the higher spectral component in the relevant region.
Table 5.3: Averaging features for AF and NSR records
AF
NSR
AF
NSR
QT1
42.82
30.08
QT9
91.94
38.88
QT2
50.83
36.78
QT10
99.56
45.00
QT3
51.50
22.90
QT11
34.07
23.79
QT4
42.26
24.42
QT12
39.82
29.41
QT5
49.88
30.54
QT13
4.66
3.43
QT6
49.68
14.45
QT14
34.14
29.19
QT7
94.31
52.98
QT15
39.62
25.4543
QT8
102.32
56.89
QT16
4.92
3.34
Figure 5.1: 16 QT features are averaged over the whole AF and NSR records
Apart from mean value of features, histograms are displayed to show the
distribution of the features. This histogram counts the number of elements
within the range of features and displays each range as a rectangular bin. The
height of the bins specifies the number of values that fall within each range.
To simplify comparison, the Y axis in Fig. 5.2 is normalized by the amount
of total heart beats and represented in percentage. Fig. 5.2 (a) refers to a
distinct feature QTCan14, which is centered on the range 30 – 45 in case of AF
(upper) and well below 20 in case of NSR (bottom). In comparison, QTCan11
in Fig. 5.2 (b) are overlapping over the range 20 – 40 in the both cases, thus
little differentiation appears to be present. The histograms show that QTCan1
and QTCan2, QTCan4 and QTCan5, QTCan7 and QTCan8, QTCan9 and
QTCan10, QTCan14 and QTCan15 are highly correlated.
52
Results and Discussion
(a)
(b)
Figure 5.2: Histogram of QTCan14 (a) and QTCan11 (b) comparing the AF
records (upper) with NSR records (bottom). QTCan11 and QTCan14 are the
power percentages in the frequency band 5 – 10 Hz using the beat window I and
using the beat window II, respectively.
5.1 Results on Belt Database
53
Table 5.4: Results of AF detection using QRST Cancellation on wearable belt
system. The measures are expressed in %. Legend:Met.-classification method,
Se.-Sensitivity, Sp.-Specificity, Pr.-Predictability.
Feature
QTCan1
QTCan2
QTCan3
QTCan4
QTCan5
QTCan6
QTCan7
QTCan8
QTCan9
QTCan10
QTCan11
QTCan12
QTCan13
QTCan14
QTCan15
QTCan16
Met.
QDC
3-KNN
QDC
3-KNN
3-KNN
QDC
3-KNN
3-KNN
3-KNN
3-KNN
3-KNN
3-KNN
QDC
KNN
3-KNN
QDC
Se.
80.77
83.21
80.77
91.56
86.04
84.19
89.61
91.80
93.59
92.45
86.77
88.72
98.07
81.51
85.61
100.00
Sp.
78.19
85.66
81.90
80.71
86.50
86.19
83.43
83.04
83.57
83.57
88.47
86.22
57.74
96.60
91.53
55.71
Pr.
64.29
78.43
78.57
71.43
78.57
78.57
71.43
78.57
78.57
78.57
64.29
71.43
50.00
85.71
78.57
50.00
These features are fed into classifiers trained to recognize the AF data from
NSR data using the training set. Different classifiers were applied: normal
density based linear classifier (LDC), normal density based quadratic classifier
(QDC), back-propagation neural network with one hidden layer of 10 neuron
units (10-ANN) and K-nearest neighbor classifier (KNN). Classification decisions on testing set were evaluated by statistical parameters: sensitivity, specificity and predictability. Table 5.4 summarized the best results using the four
classifiers noted above, which received one single QTCan feature independently.
For input of one feature KNN and QDC have the best performance on this
problem. The highest sensitivity, specificity and predictability are achieved using the feature QTCan14: 81.51%, 96.60% and 85.71%. The features using the
beat window II (QTCan4 – QTCan6 and QTCan14 – QTCan16) provide higher
sensitivity and specificity than the ones using the beat window I (QTCan1 –
QTCan3 and QTCan11 – QTCan13). The features QTCan7 – QTCan11 distinguish AF and NSR data more efficiently than the single feature QTCan1,
QTCan2, QTCan4 and QTCan5. The power spectra of the whole ECG block
of 30 heart beats (QTCan11 – QTCan12 and QTCan14 – QTCan15) yield
the better sensitivity and specificity than the mean power spectra (QTCan1 –
QTCan2 and QTCan4 – QTCan5). Here we assume that the sensitivity and
specificity are equal important, and the optimal cur-off between sensitivity and
specificity is determined by ROC curve.
The ventricular irregularity plays an important role in differentiating between AF and other irregular rhythm. To test the effect on the combination
54
Results and Discussion
Table 5.5: Results of AF detection using features of RR interval and
QRST Cancellation on wearable belt system. The measures are expressed
in %. Legend:Met.-classification method, Se.-Sensitivity, Sp.-Specificity, Pr.Predictability.
Features
RR interval
RR interval, QTCan1
RR interval, QTCan2
RR interval, QTCan3
RR interval, QTCan4
RR interval, QTCan5
RR interval, QTCan6
RR interval, QTCan7
RR interval, QTCan8
RR interval, QTCan9
RR interval, QTCan10
RR interval, QTCan11
RR interval, QTCan12
RR interval, QTCan13
RR interval, QTCan14
RR interval, QTCan15
RR interval, QTCan16
Met.
QDC
QDC
LDC
QDC
10-ANN
QDC
KNN
QDC
QDC
QDC
QDC
QDC
QDC
QDC
QDC
QDC
QDC
Se.
95.24
96.43
97.32
98.21
99.11
96.43
99.35
97.32
96.43
95.54
97.32
98.17
98.56
98.27
99.46
98.56
98.42
Sp.
94.64
96.43
95.71
96.43
96.43
97.02
94.64
95.83
96.43
97.62
96.00
98.40
98.21
98.69
100.00
98.81
98.10
Pr.
92.61
92.86
88.71
92.86
92.86
92.86
92.86
92.86
92.86
92.86
85.71
92.68
92.68
85.71
100.00
92.86
85.71
Figure 5.3: Receiver operating characteristic curve for combining features of
QTCan14 and RR interval.
5.2 Results on MIT Database
55
of features, RR interval (see section 3.4) was input the classifiers in additional
to the features of QRST cancellation. The results of AF detection using the
combined features are described in the table 5.5. As expected these combining
features improve the sensitivity, specificity and predictability of AF detection
significantly in comparison to the single feature. The best result using features
of only RR interval is sensitivity: 97.90%, specificity: 93.33% and predictability:
85.71%. The performance of KNN with multi-input features is not as superior
as it was on the single input feature. QDC classifier has a better performance,
which yielded sensitivity: 99.46%, sensitivity: 100% and predictability 100%
using combination of the RR interval and QTCan14. Figure 5.3 depicts the
ROC curve for this instance, illustrating the change in sensitivity and false positivity (expressed in percentage in the figure) determined by decision variable,
presented as diagonal cross. False positivity is defined as one minus specificity.
The decision threshold is here the number of detected AF beats (NAF = 2 . . . 29)
in sliding window of 30 beats. If the threshold exceeds NAF , the segment is
defined as AF. The cut-point 28, connected with (0,1) in red line, is optimal
trade-off between sensitivity and specificity. The fact that only chronic AF and
NSR data was evaluated without paroxysmal AF, has a big influence on the
result of 100%.
5.2
Results on MIT Database
A comparative study using MIT Atrial Fibrillation database was performed.
This database includes twenty-three long-term ECG recording of paroxysmal
AF patients. Among them, eighteen recordings were analyzed, which contain
AF as well as NSR episodes. The other five recordings (ID: 05091, 07162,
07859, 08405 and 08455) were excluded in the further analysis due to too short
or no segment of AF episodes. Table 5.6 enumerates the duration of annotated
segment in minutes as well as the number of heart beats for each record. In
total eighteen records in duration of 7937 minutes were selected to validate the
algorithms, including 246600 heart beats of AF episodes and 435600 heart beats
of NSR episodes.
The results on belt data have already given a sense, how well the features of
QRST cancellation differentiate between AF and NSR. The features extracted
from MIT data were analyzed using the same method: visualization, calculation
of the mean value and distribution histogram. These features have the similar
performance with those extracted from the belt data. Generally the most energy
derived from remainder of AF data is contained within 4 – 10 Hz, whereas the
remainder of NSR has a lower frequency content in this area. Therefore the features from AF data are greater than NSR data, except QTCan13 and QTCan16.
More distinct features were obtained using the beat window II (QTCan4 – QTCan6, QTCan9 – QTCan10 and QTCan14 – QTCan16) than using the beat
window I (QTCan1 – QTCan3, QTCan7 – QTCan8 and QTCan11 – QTCan13).
The reason will be explain in the section 5.4. QTCan1 and QTCan2, QTCan4
and QTCan5, QTCan7 and QTCan8, QTCan9 and QTCan10, QTCan11 and
QTCan12, QTCan14 and QTCan15 are strongly correlated. For example, Fig.
56
Results and Discussion
(a)
(b)
(c)
(d)
(e)
(f)
Figure 5.4: Plot features (a) QTCan4 (b) QTCan6 (c) QTCan10 (d) QTCan15
(e) QTCan2 and (f) QTCan11 of AF (red solid line) and NSR (blue dashed line)
episode from record 04746.
5.2 Results on MIT Database
57
Table 5.6: The duration of annotated segment in minutes and the number of
heart beats for each MIT AF database. ID-patient ID, Pos. HB-the number
of heart beats in AF episodes, Neg. HB-the number of heart beats in NSR
episodes, HB-the number of total heart beats in each record.
ID
04015
04043
04048
04126
04746
04908
04936
05121
05261
06426
06453
06995
07879
07910
08215
08219
08378
08343
Total
AF(min)
3.85
130.04
5.73
22.59
206.46
37.62
439.55
385.11
7.91
384.10
4.51
93.86
370
33.51
95.32
131.76
24.32
16.39
2394.45
NSR(min)
93.41
466.75
506.39
531.24
173.90
535.14
114.52
141.12
485.77
27.80
492.66
159.02
118.98
137.05
118.3
273.29
371.03
150.06
5542.20
AF+NSR
97.26
596.79
512.12
553.83
380.36
572.76
554.07
526.23
493.68
411.90
497.17
252.88
488.98
170.56
213.62
405.05
395.35
166.45
7936.65
Pos. HB
600
16170
960
3150
30900
5910
40350
33240
1110
35940
510
8730
40080
2490
7170
15060
1980
2190
246600
Neg. HB
7680
45420
34170
36750
10170
54330
6960
10800
36210
2340
31140
27570
9210
29970
10170
26820
30270
10350
435600
HB
8280
61590
35130
39900
41070
60240
47310
44040
37320
38280
31650
36300
49290
32460
17340
41880
32250
12540
682200
(5.4) plots the features QTCan4, QTCan6, QTCan10, QTCan15, QTCan2 and
QTCan11 of AF (red solid line) and NSR (blue dashed line) episode from record
04746. Obviously QTCan2 and QTCan11 can not distinguish AF from non-AF
well.
Effective features were selected by statistical methods manually: visualization, mean values and histogram. The performance of combining features was
also analyzed by varying the number of features input to the classifier. Distinct
features of RR interval and QRST cancellation were given to LDC, QDC and
10-ANN classifiers, instead of all the features due to computation constrains.
3-KNN was not applied due to its expensive computation for large amount of
data. The table 5.7 summarized the best outcoming results of these three classifiers using the feature of RR interval, QRST Cancellation and combination
of the both features. These results revealed that significant ventricular irregularity is crucial criterion to distinguish AF from NSR data, giving the best
result: sensitivity 89.85%, 89.14 % and 76.50%, in comparison to the other
one single feature. The performance of QTCan features on MIT data is not
as good as this on belt data. MIT data is much longer and noisier than belt
data, containing diverse ECG morphology, thus the analysis of MIT data is
much more complicated and difficult. Therefore the sensitivity, specificity and
58
Results and Discussion
Table 5.7: AF detection using feature of RR interval, QRST Cancellation and
combining the both features on MIT database. The measures are expressed
in %. Legend:Met-classification method, Se.-Sensitivity, Sp.-Specificity, Pr.Predictability.
Features
RR interval
QTCan4
QTCan5
QTCan6
QTCan9
QTCan10
QTCan14
QTCan15
RR interval, QTCan4
RR interval, QTCan5
RR interval, QTCan6
RR interval, QTCan9
RR interval, QTCan10
RR interval, QTCan14
RR interval, QTCan15
Met.
QDC
10-ANN
10-ANN
QDC
10-ANN
LDC
LDC
LDC
QDC
QDC
ANN
10-ANN
10-ANN
10-ANN
10-ANN
Se.
89.85
76.83
77.03
79.11
81.06
78.39
72.01
73.72
93.07
93.83
91.11
91.74
91.80
91.33
91.14
Sp.
89.14
77.45
78.65
77.25
75.29
79.50
60.32
75.31
90.38
90.12
91.96
91.53
91.62
90.04
92.01
Pr.
76.50
60.89
61.23
56.73
56.91
60.26
51.74
61.51
78.90
79.15
79.17
79.56
80.00
78.30
82.08
predictability obtained using MIT data is lower than these using the belt data,
although the same QRST cancellation algorithm was utilized. Combining the
QTCan features with the RR interval features enhance the sensitivity, specificity and predictability of AF detection. A similar sensitivity, specificity and
predictability is achieved using QTCan4, QTCan5, QTCan9, QTCan10, QTCan14, QTCan15. The combination of the features RR interval and QTCan15
have a slightly superior performance, sensitivity: 91.14%, specificity: 92.01%
and predictability: 82.08%.
5.3
5.3.1
Discussion
Limitation of QRST Cancellation
It is well known that AF is associated with chaotic and rapid atrial activity.
The estimation of atrial activity in the surface ECG based on spectral analysis
is attempted by separating ventricular activity. Subtraction of the averaged
QRST complex from each individual beat is the standard method for producing
a signal which mainly contains atrial activity – the residual ECG signal. It
is highly desired, the dominant energy lies in frequency band extending from
4 to 10 Hz, since this frequency band is identified as the atrial frequency in
fibrillatory rhythm. Unfortunately, it is not always this case in reality. The
reasons of shifted peak frequency were investigated in order to provide a precise
detection of fibrillatory waves in the future.
5.3 Discussion
59
1. Invisible Fibrillatory waves: Fibrillatory waves are often of very low
amplitude or not apparent in the ECG data. For example, few fibrillatory waves are presented in the ECG of Fig. 5.5. But the heart
beat variability of this episode is clearly higher than the NSR data,
thus it is labelled as AF.
Figure 5.5: The fibrillatory waves in this AF example (Record 04015, MIT AF
database) are not apparent.
2. Annotation: Sometimes chaotic atrial activity is observed in the ECG
data labelled as NSR (see Fig. 5.6). This chaotic waves results in
a peak or high spectral energy exhibiting in the frequency band 410 Hz. But the ventricular activity is regular relatively, thus it is
labelled as NSR episode.
3. Noise: Noise in ECG signal causes considerable errors, e.g., false detection of QRS on- and offset, missing R peak, great distances calculated
for each pair of QRS complexes, which may be denoted as abnormal
heart beat and false QRS clustering depending upon R peak detection, QRS on- and offset. More energy in the relevant frequency
spectrum range is produced by this noisy than much lower amplitude fibrillatory activity. Filters can not handle this problem in a
satisfactory way. This noise is inside the physiological bandwidth
(0.5 – 50 Hz), thus can not be eliminated by the low pass filter with
a cutoff frequency of 50 Hz. Fig. 5.7 shows an example of a noisy
signal, containing noise below 50 Hz after filtering, and an accurate
delineation of QRS complexes is impossible.
4. Insufficient subtraction This algorithm is based upon a combined
QRS and RR interval template. However a variable P wave, QT
interval and T wave may require separate templates, which are not
considered in this study. For some data, P wave, QT interval and
60
Results and Discussion
Figure 5.6: NSR example (the 6th NSR record of belt database) with chaotic
atrial activity.
Figure 5.7: ECG signal from record 06426 (MIT database) is polluted by noise
and causes considerable error in QRST cancellation.
T wave change dynamically and frequently, which have impact on
the precision of the technique. The uniform templates of P wave,
QT interval and T wave cannot match the signal well and result in
inefficient subtraction of P wave and T wave. The pieces of QRS, T
wave and P wave in the remainder increase the power spectrum in
the related frequency band.
5.4 The first and the beat window II
61
(a)
(b)
Figure 5.8: (a) One ECG example from NSR(1) of belt data (see table 5.2) in
blue solid line superimposed onto the template using beat window I in red dashed
line, remainder and its power spectrum. (b) This ECG example superimposed
onto the template using the beat window II, remainder and its power spectrum.
62
5.4
Results and Discussion
The first and the beat window II
The results on the belt database and MIT database revealed that the features
extracted by template using the beat window II distinguish AF and NSR data
better than using the beat window I. Beat window I is defined as interval form
current QRS onset to the subsequent QRS onset (from Qon (i) to Qon (i + 1)),
see Fig. 4.15 (a). In the second case, the heart beat is counted from Pon (i) to
Pon (i + 1) see Fig. 4.15 (a). The beat window I placed the P wave in the end
of beat window, whereas the second one placed the P wave in the beginning.
The location of P wave changes over a large range as a result of QT dispersion.
Consequently the subtraction using the beat window I cannot remove the P
wave efficiently, because the P waves in different locations are averaged. The P
wave has a high frequency component in the bandwidth of 4-10 Hz (specified
later in Fig. 5.10), which affects the specificity of QRST cancellation algorithm.
Nevertheless, the PR interval is relative constant, thus the template using the
beat window II represents the P wave in most heart beats accurately. Fig. 5.8
(a) shows one ECG example from the first NSR record of belt data (see NSR(1)
in the table 5.2) in blue solid line superimposed onto the template using the beat
window I in red dashed line, the remainder, as well as the its power spectrum.
Fig. 5.8 (b) shows the results using the beat window II. P wave present in
the remainder1 resulted in a peak of 5.8 Hz exhibiting in the power spectrum,
thus false detection of AF. In comparison, there are no P waves observed in the
remainder2. Therefore the peak location in Fig. 5.8 (b) is outside the relevant
frequency band.
5.5
QRS clustering Methods
Numerous clustering algorithms are available in the literature. Different QRS
clustering methods were studied. Choosing the correct number of clusters is a
challenge. Too few clusters result in QRS complexes with very different morphologies being clustered together, and thus inaccurate templates. Too many
clusters result in too few QRS complexes being used to train each cluster and
results in over-fitting.
One possible approach of unsupervised QRS clustering is to form a plurality
of different templates by placing the first QRS complexes within a first cluster
and comparing the second QRS complexes to the beat window I derived by
averaging the QRS in the first cluster. If, for instance, the second QRS complex
conforms to the beat window I within a certain degree set by a threshold, the
second QRS complex is placed in the first cluster, otherwise forms a second
cluster. In each iteration the templates are updated when the new beats are
allocated to it. Each subsequent QRS complexes is compared to the existing
templates and is either grouped into the one that provides the closest distance
or forms a entirely a new cluster. The clusters contain one or two heart beats
are labelled as anomalous heart beats and excluded in the further analysis. If
too many heart beats fall into abnormal groups, the steps for establishment
of cluster structure are repeated with a increased threshold. This algorithm
5.5 QRS clustering Methods
63
yielded similar results to the algorithm described in section 4.2.2 on ECG block
in short length. But this time-consuming algorithm is not suitable for large
database. Hence this algorithm is not incorporated in AF detection framework.
(a)
(b)
(c)
(d)
Figure 5.9: (a) Ensemble QRS complexes in the example of Fig. 4.9. (b) – (c)
Three clusters created by K-mean clustering. The number in title of each figure
is the cluster size.
A conventional clustering algorithm: K-mean clustering was tested, which
is one of the simplest unsupervised learning algorithm. K-means treats each
observation in the data having a location in space. It finds a partition in which
objects within each clusters are as close to each other as possible, and as far
from objects as possible. Each cluster in the partition is defined by its centroid.
The centroid for each cluster is the point, to which the sum of distances from all
objects in the cluster is minimized. K-means uses an iterative algorithm that
minimizes the sum of distances from each object to its cluster centroid, over all
clusters. This algorithm moves objects between cluster until the sum cannot be
decreased further. The algorithm is composed of the following steps [47]:
1. Place K points into the space represented by the objects that are being
clustered. These point represents initial group centroids.
2. Assign each object to the group that has the closest centroid.
64
Results and Discussion
3. When all objects have been assigned, recalculate the position of K centroids.
4. Repeat steps 2 and 3 until the centroids no longer move, that means the
sum of distances from each object to its cluster centroid are minimized.
Fig. 5.9 depicts partitioning the QRS complexes in the example (see Fig.
4.9) into three clusters, created by K-mean clustering. Note that the anomalous
heart beats are not separated and fall in incompact cluster 2. Furthermore, the
objects in cluster 1 are not distinct from these in cluster 2. The last problem
is particularly troublesome, since we do not know how many clusters the heart
beats in ECG block should be partitioned in. Therefore, K-mean clustering is
not incorporated in AF detection framework, either.
5.6
Survey of QRST cancellation using appropriate
templates
In the study [33] it was proposed to obtain the fibrillatory frequency using QRST
cancellation algorithm with appropriate templates. Appropriate templates are
adopted by comparing each QRST interval with the mean duration for all the
heart beats. Each QRST interval is then determined to occur from its own
onset to the onset of subsequent QRST interval. The heart beat is considered
to be ectopic if the duration of QRST exceeds the product of 1.2 times mean,
otherwise to be normal heart beats. Based on the comparison, the QRST
complexes are either added and averaged in with a conducted a template or an
ectopic template. Based on the result of this step, the signal processor selects
the appropriate template and subtracts the normal and ectopic templates from
the corresponding heart beats respectively. This technique does not take the
variation in QRS morphology into account, which occurs very frequently. More
than 10% of ECG recordings were not analyzable using this technique.
Hnatkova stated an approach to compute the templates of QRS and T wave
and subtraction of QRS and T wave separately, based on self-similarities of the
ECG signals in [48]. Self-similarities between corresponding QRS and corresponding T wave were accessed using correlation coefficients. It was performed
in two steps; the first phase evaluated the template, and the second phase estimated the similarity using correlation coefficients. The first QRS sample was
identified with the predefined window, 200 ms window around the QRS fiducial point. The cross-correlation coefficient was computed within a segment of
the ECG that followed/preceded the fiducial point by the same distance. The
point and the value of correlation coefficient was saved for each QRS sample.
The pair of ECG samples giving the maximum correlation, which exceeded the
predefined cut-off was then selected as basic template. If no basic template at
the upper required correlation coefficient the next ECG sample was tested. The
second step of the algorithm used the basic template which was correlated with
the rest of ECG signal. The above described method was again utilized. The
remaining un-matched ECG were repeatedly tested with the required crosscorrelation decreased in steps of 0.01 until the lower limit was reached. The
5.6 Survey of QRST cancellation using appropriate templates
65
actual content of each QRS sample was again used to update the template. In
such a way, there was assigned a template for most of ECG samples. The same
methods was utilized to compute the template for T wave. The generation of
T wave template used a 450 ms window, which started 100 ms after the QRS
fiducial point.
The study population consisted of 23 patients, but only in 20 was QRS
and T wave subtraction feasible. According to the experience, the recognition
of T wave requires clean signal. In the case of atrial fibrillation, a less clean
signal is present due to continuous atrial fibrillatory wave in the signal. For
instance, it’s difficult to delineate T wave in Fig. ??, where the fibrillatory
waves are superimposed into T waves. An variable QT interval requires separate
template. This algorithm generated only one most common template, which is
not suitable under some circumstances, e.g., the AF example in Fig. 4.13.
Figure 5.10: Frequency spectrum of the entire P wave presented in 10 – Hz
frequency bands in patients and controls. PAF: paxoxysmal atrial fibrillation.
adapted from [49]
In the beginning of the final thesis, only the templates of QRS complexes
and T wave were subtracted from ECG signal instead of the mean heart beat.
As a result, some remainder of the NSR data exhibited a high spectral energy
in 4 – 10 Hz frequency band, which did not allow the differentiation of AF
from NSR. Fig. 5.10 depicts the high frequency contents from 4 to 10 Hz in
the power spectrum of entire P wave in the case of PAF and NSR enclosed in
yellow area. Fig. 5.8 (a) shows the frequency spectrum of a remainder of NSR
data presented with P waves.
66
Results and Discussion
5.7
Results summary
Feature extraction was performed both automatically using a decision tree
structure and manually by looking at different scattered plots and statistical
parameters such as the correlation matrix. Only two features as an input for
classifier were selected. We found that automatic analysis delivered one feature
from the R-R interval analysis (fifth element of Moody matrix RR5 [18]) and
one feature from the group of P template matching (number of found P waves in
the window of 30 beats long-PtemMatch). In manual selection we concentrated
on the selection of parameters only from the first R-R group in order to obtain
less computational expensive system (first and sixth element of Moody matrix
[18] - RR1 and RR6 ). The results are summarized in Table 5.8.
Table 5.8: AF detection using wearable belt system incorporated to intelligent
textile. The measures are expressed in %. Legend:Met-classification method,
V.Er.-validation error, Se.-Sensitivity, Sp.-Specificity, Pr.-Predictability.
Feature
RR1,PtemMat,QTCan
RR5,PtemMat
RR1,RR6
Met.
LDC
QDC
3-KNN
10-ANN
LDC
QDC
3-KNN
10-ANN
LDC
QDC
3-KNN
10-ANN
V.Er.
2.65
2.67
0.71
0.69
4.50
3.68
3.43
2.24
4.63
3.65
7.52
2.96
Se.
99.32
99.46
99.35
99.11
98.13
98.27
98.17
99.85
97.90
95.24
92.25
95.10
Sp.
95.71
100
94.64
96.43
96.79
98.10
95.12
96.31
93.33
94.64
91.79
94.64
Pr.
88.71
100
92.86
92.86
78.57
85.71
85.71
85.71
85.71
92.86
85.71
92.86
Figure 5.11: Decision curves comparison demonstrated on R-R interval parameters.
In terms of the error on the validation data (beat-to-beat based detection),
QDC yields the best results in both cases. After post-processing of classifier
5.7 Results summary
67
output - see Fig. ??, the results are summarized using sensitivity, specificity
and predictability measures. The decision surface curves for the R-R features
are depicted in Fig. 5.11.
Figure 5.12: Performance comparision using ROC approach. The decision
threshold is here the number of detected AF beats (from NAF = 2 . . . 29) in
sliding window of 30 beats. If the threshold exceeds NAF than the segment
is labelled as AF. OT O is the optimal trade-off between Sensitivity and Specificity.Legend: ’dash-3KNN’, ’solid-LDC’, ’dot’-QDC, ’dashdot’-10ANN.
Since the classifier results are further post-processed in a sliding window, a
design parameter determining the final segmentation classification can be used
to obtain receiver-operating curve. This threshold determines if the segment is
AF or not-see Fig. 3.10. By tuning the threshold parameter in the fix window
of 30 beats, the optimal trade-off between sensitivity and specificity can be
found-see Fig. 5.12. The optimal trade-off is defined as the minimum distance
between the point (0,1) and the points on the ROC curve. It can be seen that
best results in terms of sensitivity are achieved using with combination of three
features RR5, PtemMat, QTCan - PR ratio in 5-10Hz frequency band with
combination of ANN classifier.
Finally, in the last two Fig. 5.13-5.14, the density estimation of underlying
data is shown. The two densities are slightly overlapping resulting in final error
3.6% on validation data set.
Figure 5.13: Bayes density estimation of underlying data. The covariances
matrices are not equal which result is quadratic discrimination function.
68
Results and Discussion
Figure 5.14: Density estimation in three dimensional space.
In case of MIT-Database, the results are depicted in Table 5.9. The numbers
are not so high like in case of Belt data due to bigger presence of noise and
artifacts in Holter recordings that were used for MIT AF Database creation.
Table 5.9: AF detection using MIT AF Database. The measures are expressed
in %. Legend:Met-classification method, V.Er.-validation error, Se.-Sensitivity,
Sp.-Specificity, Pr.-Predictability.
Feature
RR5,PtemMat,QTCan
RR5,PtemMat
Met.
LDC
QDC
3-KNN
10-ANN
LDC
QDC
3-KNN
10-ANN
V.Er.
4.32
3.61
NA
2.12
4.50
3.68
NA
2.24
Se.
90.00
93.83
NA
91.45
89.23
91.15
NA
91.65
Sp.
84.68
90.12
NA
92.01
86.12
87.96
NA
87.47
The similar comparison proceeding was carried out like in case of Belt Database. In Fig. 5.15 the four classifiers are compared. The best separation of both
classes is achieved by QDC classifier again. To visualize better its statistical
characteristic, the density function in two dimension in form of contours and its
three dimensional counterparts are shown in Fig 5.16, Fig. 5.17 respectively.
5.7 Results summary
Figure 5.15: Decision curves comparison demonstrated on R-R interval and P
wave template matching parameters.
Figure 5.16: Bayes density estimation of underlying data. The covariances
matrices are not equal which result is quadratic discrimination function, MIT
AF Database.
Figure 5.17: Density estimation in three dimensional space, MIT AF Database.
69
6 Summary and Perspective
AF is common arrhythmia, arising from the disturbance of initiation and propagation of excitation pattern in atrial tissue. In the ECG AF is indicated
by irregular ventricular rhythm, absence of consistent P wave and presence of
random fibrillatory waves. An automatic method for the detection of atrial
fibrillation has been developed, differentiating the ECG signals with AF and
with NSR.
The QRST cancellation algorithm was evaluated for two databases: the belt
databases, collected from 8 chronic AF patients and 6 healthy subjects at rest
using a chest belt with dry electrodes yielding 1 lead ECG; and 23 long-term
ECG of paroxysmal AF patients including AF episodes and NSR episodes from
MIT-BIH atrial fibrillation database. Based upon straight forward averaging
algorithm, the QRST cancellation algorithm was improved to generate appropriate template. This algorithm combined QRS clustering and RR interval
classification, so that the template presented the individual heart beat more
accurately. This algorithm is comprised of four major steps.
• First, the ECG signals were preprocessed, and only the region between
0.5 – 50 Hz was kept for analysis to avoid contributions from noise of high
frequency noise, baseline wonder and power-line interference. Fiducial
points were detected using our existing processing system.
• In a second step all beats were divided into classes of heartbeats with
similar QRS morphologies and ventricular cycle length using unsupervised
approach. Abnormal heart beats were rejected in the further analysis. For
each class of normal heartbeat, an averaged heartbeat was calculated as
appropriate template. Hereby two definitions of heart beats window were
tested.
• The third step was the subtraction of the corresponding averaged heart
beats from the original sequence after alignment with each QRS complex
in order to remove the regular ventricular signal parts.
• Finally, the residual signals were subjected to Fast Fourier Transformation
to analyze the power spectrum. Regarding the survey on frequency spectrum of AF, the features which reflects the electrophysiological changes
manifest in the power spectrum of AF remainder, were extracted in a sliding window consisting of 30 beats. The results revealed that the energy
and spectral peak of remainders are concentrated on frequency band 4 –
10 or 5 – 10 Hz in the case of chronic AF patients, whereas the remainder
of NSR data has a lower frequency component.
72
Summary and Perspective
The features of power percentage and peak location were handled into various classifiers for pattern recognition which classify the ECG into two classes:
AF present and AF absent. The outcomes of classifiers are post-processed to
give a optimal cutoff between sensitivity and specificity. Finally, the accuracy
of classification decision were measured by statistical parameters. The best
results of sensitivity: 99.46%, specificity: 100.00% and predictability 100.00%
was achieved on belt data. The best result on MIT data is sensitivity: 91.14%,
specificity: 92.01% and predictability: 82.08%.
In this study the QRST algorithm which accounts for rapid variations in
QRS morphology and RR interval, were developed and validated on two databases. The results show that this method performs considerably better than
does straightforward averaging method. Application of QRST cancellation
features allowed improved differentiation of AF from other non-AF irregular
rhythm than using the RR interval series alone. The method for automatic AF
detection using simple features along with LDC, QDC, KNN and ANN classifiers has been presented. The system works with high sensitivity, specificity
and predictability.
There is some room to improve the QRST cancellation algorithm. Supervised clustering methods can be tested, e.g, artificial neural network. The
variation in P wave, T waves and QT interval should be taken into consideration in the further work. The remainders of P wave and T wave contribute
to high spectral power in the relevant frequency band, which affect the sensitivity and specificity of the algorithm. For classifiers a hierarchical structure
could be established, in which the features are combined in a weighted fashion. RR interval which plays an important role in a differentiating between AF
and other irregular rhythm, should be considered at first level. This approach
would lead to a system of AF detection with personalized feature extraction
and personalized classifiers for individual recording in the future.
A Abbreviations and Acronyms
AF
AP
APD
APG
AV
AVR
bpm
CHF
CS
CT
ERP
JSR
NSR
MDP
SR
WM
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
atrial fibrillation
action potential
action potential duration
right appendage
atrioventricular
atrioventricular ring
beats per minute
congestive heart failure
coronary sinus
crista terminalis
effective refractory period
junctional sarcoplasmic reticulum
network sarcoplasmic reticulum
maximum diastolic potential
sinus rhythm
working myocardium
B COOKING BOOK FOR AF
DETECTION TOOLBOX
I. Configuration
1. Open Config AFDetection.m and AFConfig.m and edit the paths
where your data and your source code is saved.
2. Call Config AFDetection.m to install the toolbox
II. Segmentation (R,P waves peaks detection) Both database
1. Open SegmentationSignal.m
a) Choose database,e.g. Database=’MIT’;
b) Uncomment the section for MIT or Own database
c) The files will be save to D:\AtrialFibrillationData\
, e.g. Segmentationnor_a_050725_1.mat
III. Noise Level Detection
In case of OWN database, an algorithm for noise section determination
was developed. Just start DetectNoise.m.
IV. Feature Extraction
MIT Data - Small Training & Test set = validation set
1. P wave template selection
a) The nice P waves was selected for each signal in SelectPWaveforMITDat.m
b) First, the appearance of P waves for each signal is defined using
variables NPwave_Segment_lengthTEMP=2;
StartNormalTemp=60+Sampling_Rate*60*1;
EndNormalTemp=StartNormalTemp+Sampling_Rate
*60*NPwave_Segment_lengthTEMP;
c) than
the
longer
segment
is
defined
for
evaluation
of
the
selection
using
ROC
analysis
NPwave_Segment_length=20; StartNormal=EndNormalTemp;
EndNormal=StartNormal+Sampling_Rate*60*NPwave_Segment_length;
d) E.g.,
the
following
file
will
be
generated:
PwaveTemMat04015_1.mat
76
COOKING BOOK FOR AF DETECTION TOOLBOX
2. Open AFProcessingBatch.m
3. Choose channel Channel=1; and functionality
FUNCTION_BATCH=’ExtractFeaturesSignal’;
4. In ExtractFeaturesSignal.m are for each signal defined the representative segment of NSR (normal sinus rhythm) and AF data. The following file will be for example produced FeatExtract04936_1.mat.
5. If you want to change training set than you must redefine the segments in ExtractFeaturesSignal.m, e.g.
case ’04015’;
%Very few data of AF - no possibility of
merging training and validating set MERGE=0;
%Train-Normal-BAD example:StartNormalTrain=30
NPwave_Segment_lengthNormal=3; NPwave_Segment_lengthNormal
actually means Segment_lengthNormal, and has nothing to do with
the normal P-wave. (MH)
StartNormalTrain=166857+Sampling_Rate*60*21;
%AnnotECG.Beg_Seg_N_All(4)
EndNormalTrain=StartNormalTrain+Sampling_Rate*60*,...
NPwave_Segment_lengthNormal;
%Train-AF NPwave_Segment_lengthAF=2; StartAFTrain=133348;
%AnnotECG.StartAF(3);
EndAFTrain=StartAFTrain+Sampling_Rate*60*NPwave_Segment_lengthAF;
%Validate-Normal NPwave_Segment_lengthNormal=3;
StartNormalTest=166857+Sampling_Rate*60*25;
%AnnotECG.Beg_Seg_N_All(4)
EndNormalTest=StartNormalTest+Sampling_Rate*60*,...
NPwave_Segment_lengthNormal;
%Validate-AF NPwave_Segment_lengthAF=2; StartAFTest=133348;
%AnnotECG.StartAF(3);
EndAFTest=StartAFTest+Sampling_Rate*60*NPwave_Segment_lengthAF;
6. The Validation set must be created by merging the already extracted
feature files like FeatExtract04936_1.mat. This is done by calling
ComposeTrainingSet.m.
You can specify which record should
contributed to validation set by filling variable DataID, e.g.,
DataID={’04015’;’04746’;’04908’;’04936’;’06995’;’07879’;’08219’;’08378’};
The
following
file
will
be
generated
:
FeaturesTrainingValidationAll1.mat
MIT Data - BIG Testing set
1. Open AFProcessingBatch.m
2. Choose channel Channel=1; and functionality
FUNCTION_BATCH=’ExtractFeaturesWhole’;
77
3. The features will be selected for the whole signal. Note, that this is
quite consuming task, normally it takes like one day.
OWN Data - The whole data set
1. P wave template selection
a) No patients suffering from paroxysmal AF are presented. Therefore the P wave template selection was selected by trial & error for
all the NSR records using SelectPwaveOwn.m
b) The following file will be generated PwaveTemMatOwn.mat
2. Open AFProcessingBatchOwn.m
3. Create
Annotation
using
functionality
FUNCTION_BATCH=’CreateAnnotationOwn’;
4. Choose functionality FUNCTION_BATCH=’ExtractFeaturesSignal’;
5. In ExtractFeaturesSignalOwn.m are for each signal defined
the
representative
segment
of
NSR
and
AF
data.
The following file will be for example produced
FeatExtractOwnFullnor_a_050725resampleTrainNormal.mat
6. If you want to change training set than you must redefine the segments in ExtractFeaturesSignalOwn.m, e.g.
case ’ned_r_070440resample’; WhichLabel=’Normal’;
WhichSet=’Train’; Start=84397; End=179931;
%6.4m
7. The OWN database is quite small, that means that training set and
testing set is equal to sets of extracted features for the whole database. To compute it, open AFProcessingBatchOwn.m
8. Choose functionality FUNCTION_BATCH=’CreateTrainingSet’;
9. The
following
files
will
be
generated:
ValidationSetOwnFullNormal.mat
and
ValidationSetOwnFullNormal.mat
V. Classifier Training
For both databases
1. The training is done using PrToolsTrain.m. For training the pattern
recognition toolbox is used !!!
a) Choose appropriate database, e.g . DatabaseAF=’Own’;
b) Choose features to be used, e.g.
FS=[1 6];
c) Choose classifiers, e.g. W1 = qdc(C);
d) You can also enable scatter plots by SCATTERPLOT=1;
e) Or automatic feature selection by FEATURESELECTION=1;
VI. Classifier Evaluation
It is quite easy because the GUI was developed. Just start SignalEvaluation.m
78
COOKING BOOK FOR AF DETECTION TOOLBOX
MH Notes:
Most of the training and testing optimisation is carried out on a small subset
of the MIT AF database (called ’Signal’. The whole database is called ’Whole’.).
This small subset is divided into two parts, called training and test. The pwave template determination is carried out on this small subset of the MIT AF
database. 10 p-wave are selected randomly from the training part of this small
subset, and tested on a fixed set of p-waves from the test part of this subset.
This process is repeated until the template matches the test p-waves well.
1. AFProcessingBatch.m carries out all sorts of batch functions, given by
FUNCTION_BATCH.
2. FUNCTION_BATCH=’ExtractFeaturesSignal’ generates the features (using the p-wave templates just generated, among other things) for the
’Signal’ part of the database.
3. ExtractFeaturesSignal.m extracts the features for the hand found start
and end points of the NSR (normal sinus rhythm), which are hard coded
in the .m file.
4. NPwave_Segment_lengthAF actually means Segment_lengthAF, and has
nothing to do with the normal P-wave.
5. The start and end times of the normal and AF segments were found by
hand (using ReadOwn.m which uses eegplot.m).
StartNormalTrain=166857+Sampling_Rate*60*21; The 166857 is from
the annotation file (the starting point of a normal segment). It was found
that this start was noisy, and the first 21 minutes were skipped.
6. If have path problems, can call Config AFDetection.m. This updates the
Matlab path variable.
7. Various paths are stored in AFConfig.m. This defines various DataPath
variables. SelectPWaveOwn.m uses the p-waves from certain patients to
generate a p-wave template. It was found that it was better to only use
one patient to generate this template rather than using all the patients.
For many of the patients, the p-wave boundaries were poorly determined,
resulting in a poor template. This could be improved by improving the
p-wave detection algorithm (or tuning it). The best p-wave boundary
detection occurred with patient ned_r_070440resample, and this was
taken to generate the p-wave template.
8. Need to add the annotation directory for our data.
9. To create the annotation, need to change if 0, if 1 at various places in
the code. (Line 200 in ExtractFeaturesSignalOwn.m.) Now changed to
CreateAnnotationOwn.m.
79
10.
a) For our data set, the full feature matrix was created, done using
FUNCTION_BATCH=ExtractFeaturesSignal. From this matrix, the
training and test set feature matrix was extracted in CreateTrainingSet.m.
b) For MIT DB, it is time consuming creating the full feature matrix
for the whole data set (can take 1-2 days). For this reason, a validation set was created (a small training and test set) for which the
features can be quickly computed. When the features work well on
the validation set, the full feature matrix for the whole data set can
be computed. In theory, the validation set matrix can be extracted
from the whole data set matrix. The validation set feature matrix
was created using FUNCTION_BATCH=ExtractFeaturesSignal. The
whole feature matrix was created using
FUNCTION_BATCH=ExtractFeatureWhole.
11. In PrToolsTrain, A is structure containing all stuff necessary for the pattern recognition toolbox. Can examine A using struct(A).
12. In PrToolsTrain.m, the validation training set is divided into a sub training set (30chosen randomly from the PR Toolbox (using the command
[C,D] = gendat(A,0.3);). The sub test set is used to evaluate the classifier trained using the sub training set. This process can be repeated
several times, giving different classifiers each time (trained from different
sub training sets, with different initial conditions), and evaluated on different sub test sets. This can be repeated until a satisfactory classifier is
obtained.
13. TestWholeSignalDiffClassifier.m tests the newly generated classifier on
the test part of the validation set.
C The M-file structure of AF
detection using MIT [1] and
OWN database
*NUBU - Abbreviation of NotUsedButUseful directory. All files that are not
currently used were placed under those directories.
• AFProcessingBatch.m - Segmentation, Feature extraction and Testing is
done for the whole AF database
• AFProcessingBatchOwn.m - Feature extraction and Testing is done for
the whole OWN database
DatabaseTools
• SegmentationSignal.m - It performs signal segmentation.
– SegmentationPeace.m - Pan Tompkins algorithm implementation
[50], pp.187.
. rdsign212.dll - Read data in 212 format, the source code is in
rdsign212.c
. readheader.m - Read MIT header, (ReadMITData.m - read data
in 212 format)
• ReadOwn.m - Read OWN Belt data and manually select segments without
noise
FeatureExtraction
• SelectPWaveforMITDat.m It is necessary to select suitable representative
P wave. Select suitable P wave. It evaluates the selection using template
matching with correlation coefficient and dynamic time warping as measures. This function can be also view as a training procedure for P wave
suitable selection and use ONLY the template matching for AF detection.
– ReadAnnot.m - Read annotations of AF database (here is also selected the channel!!!.
– SelectPwaveTemplate\_Method.m - Performs template selection
. ReadSegData.m - Reads AF data into first nth blocks, performs
segmentation
82
The M-file structure of AF detection using MIT [1] and OWN database
. TemplateComputation.m - Select P wave template
. CorrCoefCalc.m\verb - Compute correlation coefficient [50],
pp. 95.
. Dtw.m - Compute Dynamic Time Warping measure RRIntervalTraining.m computes the Moody matrix [18]. However, during
testing we use the matrix provided in Moody article.
• SelectPWaveOwn.m- Select COMMON P wave TEMPLATE from Normal
sinus ECG signals of our OWN database.
• ExtractFeaturesSignal.m It extracts features from signal. We repeat it
several times by calling ExtractFeatures.m to get the training and testing
set (validation set) for classifier.
– ComputeMatrix.m - RR intervals matrix [18].
– entropy.m - Compute Shanon entropy
– sdann.m - Compote RR time parameters SDANN and SDNNind (index)
– hrtach.m - Constructs evenly sampled rrintervals - interpolate RR
interval for further frequency analysis
– hrpowsfp.m - Compute FFT Spectrum in different frequency bandth
– QTCancellationInt.m - QT calculation and feature calculation [21].
P wave matching
. time_normalization.m - normalization of beats to have the
same length.
. TraceSegmentation.m - the method for normalization is polygonal approximation
. CorrCoefCalcTestInt.m - template matching using correlation
coefficient or DTW.
ClassifierTraining
• ComposeTrainingSet.m - It creates training set for classifier.
• PrToolsTrain.m - Training and testing using Pattern Recognition toolbox
from Delf university.
ClassifierTesting
• TestWholeSignalDiffClassifier.m Classifier TESTING for MITAF &
OWN database
– ComputeClassifierROCMITAF.m. ROC analysis over the whole AF
and OWN Belt database. Classifiers can be compared by loading
ROC curve from ClassName ROC.mat file.
– EvaluationGraph.m - Results evaluation
83
. Plotgui.m - Prints out a graph containing the rr intervals and
detected AF
• PrintClassifierAFDetectionResultsROC.m. Print the results of classifier testing for particular record.
• NUBU\NeuralNetworkTrain.m ANN training [51]. It uses Neural Network
Matlab toolbox.
• NUBU\NeuralNetworkTestValidationData.m Because the testing of one
signal takes several hours we firts test the ANN perfomance on smaller
test of data. Using this set, the best feature combination and ANN topology is determined and consequently the whole signals are tested using
NeuralNetworkBatch.m.
– NUBU\NeuralNetworkBatch.m - ANN testing
– NUBU\NeuralNetworkTestWholeSignalPCA.m - All features selected
by PCA analysis
– NUBU\NeuralNetworkTestWholeSignalRRP.m - Only RR matrix and
P wave template matching
– NUBU\NeuralNetworkTestWholeSignalRR.m- Only RR matrix as
feature
Personalization - so far only in case of NN Mathworks toolbox
The main goal is to develop algorithm adapted to each patient. Most m files
are the same for common (training over the whole database) and personalized
approach. Some kind of personalization is already P wavelet matching. The
main idea is to have for each patient adapted NN. Called by AFProcessingBatch.m
• NUBU\NeuralNetworkTrainPersonalize.m - Training
• NUBU\NeuralNetworkTestWholeSignalRRPersonalize.m\ - Testing for
RR feature
• NUBU\NeuralNetworkTestWholeSignalRRPPersonalize.m- Testing for
RR+P feature
• NUBU\NeuralNetworkTestWholeSignalPCAPersonalize.m - Testing for
RR+P +QT feature
• \NUBU\PrintMITAFDetectionResultsROCPersonalize.m - Results evaluation using ROC curve
File structure of AF detection - AtrialFibrillation directory
• AFDetectionDemos - demos for AF/OWN Belt database
• ClassifierTesting - Classifier evaluation with help of ROC curve
84
The M-file structure of AF detection using MIT [1] and OWN database
• ClassifierTraining - Classifier Training using PRTools toolbox
• DatabaseTools - Databases records Reading and Segmentation
• FeatureExtraction - RR interval, Pwave template matching, QT cancellation FeatureExtraction\FeatExtUtils - support files for extraction
File structure of MIT-AF [1] database detection
• D:\AtrialFibrillation - the above mentioned m-files are placed in this
directory
• D:\AtrialFibrillation\Doc - it includes results excel tables
• D:\AtrialFibrillation\help - it includes help for AF Detection Toolbox
• AtrialFibrillationData\!!AfExtract\FeaturesTrainingbyHong
feature extraction of both channels fir training computed by ExtractFeaturesSignal.m (45 features in window of 30 beats)
• AtrialFibrillationData\!!Af\ExtractWholeSignalbyHong - feature
extraction for the whole signals computed by AFProcessingBatch.m
(FUNCTION_BATCH=’ExtractFeaturesWhole’)
• AtrialFibrillationData\!!Af\ValidationSet - Validation set for classifier training created by ComposeTrainingSet.m
• AtrialFibrillationData\!!Af\WEKA - Conversion of Validation set for
WEKA system
• AtrialFibrillationData\!!Af\Results - results of AF detection computed by TestWholeSignalDiffClassifier.m
• AtrialFibrillationData\!!Af\Results\ANNxxx - results of AF detection for both channels and personalizes approach computed by NeuralNetworkBatch.m
• AtrialFibrillationData\!!Af\PwaveTemMat - P wave Templates for
both channels computed by SelectPWaveforMITDat.m
• AtrialFibrillationData\!!Af\Segmentation - segmentation for both
channels computed by SegmentationSignal.m
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