Download (714616727) Project2015

EEEN30160: Biomedical Signal Processing Project
Classifying PVCs in ECG
The Goal of this project is to identify Premature Ventricular Contractions (PVCs) in ECG
waveforms. It will involve ECG signal filtering and artifact removal; detection, segmentation
and extraction of discriminative features from QRS complexes, and classification of normal
vs PVC beats based on those features.
Premature ventricular contractions (PVCs) are the most common cause of irregular heart
rhythms. Normally, the heartbeat is created by an electrical signal that originates in the
sinoatrial (SA) node in the heart’s upper right chamber, the right atrium. The electrical signal
moves
through
theheart.
heart From
to thethe
atrioventricular
(AV) node,
a cluster
of specialized
cells in down
the centre
of the
AV node the signal
passes
along special
‘Purkinje‘
fibres embedded in the heart walls to the ventricles, the lower chambers. When the
electrical current arrives in the ventricles, it causes them to contract and pump oxygen-rich
blood out to the body.
A premature ventricular contraction (PVC) is a ventricular contraction that is
mistakenly initiated by the purkinje fibres of the ventricles ahead of the normal initiation by
the SA node, which causes an early, inefficient contraction before the atria have managed to
fill the ventricles. The SA node rhythm typically persists despite this early contraction, and
its signal arrives in on top of the PVC causing a sort of double-contraction of greater force
than normal.
When a PVC occurs as a single premature beat, patients may describe the feeling as
a "palpitation" or "skipped beat." But when PVCs cause these double-contractions it can be
strong enough to cause pain or discomfort in the chest. Individuals who have frequent PVCs
or a series of them may experience a fluttering sensation in the chest or neck. If PVCs are
frequent enough to reduce the heart’s pumping ability, the individual may experience
weakness,
fainting.
Whodizziness
is at riskorfor
PVCs? Almost everyone has PVCs at some time, from childhood
through adulthood. In a study of healthy members of the military, the incidence of PVCs was
0.5% among those under the age of 20 and 2.2% in those over the age of 50. PVCs occur
more commonly in older people and in individuals with underlying heart disease, including a
history of heart attack. People with a family history of cardiac arrhythmias (abnormal heart
rhythm) also have a higher risk for PVCs.
Figure 1 shows a snippet of the ECG waveform you will examine in this project, after
it has been cleaned up. It is easy to see by eye the compounded Purkinje-then-SA initiated
contractions giving rise to a greater and broader QRS deflection. The data file
ecgproject.mat contains a noisy ECG recording sampled at 200 Hz with 400 beats in it
(‘ecg1’), 93 of which are PVCs (indicated by the vector ‘isPVC’). Your job is to process the
signal and extract features which can accurately classify each beat as normal or PVC.
Fig. 1. premature ventricular contractions are the 4th and 6th beats
Specific Tasks
1. Plot the ECG signal in the time-domain, in units of sec. Try to identify a few PVC beats
versus normal beats by zooming in. Is it possible before cleaning up the data?
2. Implement filtering methods to remove high-frequency and low-frequency noise and
artifacts in the signal. What kind of artifacts did you find? Are they the kind that require
rejection of segments of data, or can they be corrected? If so, how?
[hint: sometimes filters produce ‘edge artifacts’ at the beginning and end of the data
recording being filtered. A neat trick to reduce these is to extend the data at the ends either
by ‘wraparound’ or by reflection, then filtering, then shaving off the extensions]
Provide plots that demonstrate how the signal quality has been improved by your signal
processing
steps.
Again
tryimplement
to identify an
a few
PVC beats
versus
normalthe
beats
3. Now come
up with
and
algorithm
which
segments
databy– zooming
i.e., findsin.
each of the individual beats in the overall signal and extracts segments containing them,
aligned at some particular point in the single-beat waveform (e.g. P onset, P peak, R peak, Q
or S trough, …). You can consult the internet for fancy methods for QRS complex detection,
such as the Pan–Tompkins method, but if you use them you must provide a full description
of exactly how they work in your own words, and give the source of any code you
downloaded.
4. From the segmented beats, select and extract features that you think will potentially
discriminate normal from PVC beats. Again you may consult the internet for features but
must demonstrate your understanding of them in the report.
5. Implement a classification method to classify each beat as a normal or a PVC. Partition the
data into training and test sets – indicate exactly how you have tested/validated and why.
How did you select the features? Which turned out to be the best? Compute the accuracy of
classification by comparing against the labels provided in isPVC. How many true- positives,
false-positive, true-negative, and false-negative were there? Note beats not detected and
false detections of beats (if any) by your program.
Use graphs to explain your results as necessary. Always label the axes of your graphs and
show the proper units.