Download Comparison on Time Basis of Atrial Fibrillation with Normal Sinus

International Journal of Electronics and Computer Science Engineering
Available Online at www.ijecse.org
1042
ISSN- 2277-1956
Comparison on Time Basis of Atrial Fibrillation with Normal
Sinus Rhythm ECG on MATLAB
Rajan Chaudhary 1, Santosh Kumar
2
Department of Electronics and Communication Engineering
Invertis University, Bareilly, India 1, 2
1
[email protected]
2
[email protected]
Abstract- ECG signals i.e. electrocardiogram are the significant physiological signals. In spotting and diagnosis of lots of cardiac
arrhythmias, analyzing ECG signals is a crucial task. ECG signals can be generated severally by heart construction as a unique system,
via heart compression. Atrial fibrillation (AF) is the almost usual confirmed heart beat arrhythmia and it disrupts the heart’s normal
beating rhythm. This research paper introduces a novel access for real-time simulation of the cardiac kinetics whereas evaluated ECG
signal recording is the source for the activation works for the model heart. This is inducing the classification very prompt and easy. The
initial outcomes are extremely urging. Classification and comparison of Sinus heart signal (Normal) with Arrthymia (abnormal) heart
ECG signal is established as a result.
In this paper result obtained showing that Comparison of Atrial Fibrillation with Normal (Sinus) ECG signal. Through this method we
can compare any type of disordered signal of heart.
Keywords- Atrial fibrillation; ECG; simulation; electrical activity; MATLAB
I.
INTRODUCTION
Atrial fibrillation (AF) is the almost usual disorder of cardiac rhythm and in UK it strikes up to 500,000, in Canada 350,000 almost
7 million (will be double within the next 40 years) in the USA and Europe of people. In a UK legion, the preponderance of AF was 5
per 1,000 in females, and 8 per 1,000 in males, and with an incidence of 0.2 new cases per 1,000 patient years in females and 0.9
new cases per 1,000 patient years in males [2]. Age, sex and the occurrence of some other similar maladies are directly related to the
raised incidence, including high blood pressure, coronary failure, diabetes, ischemic cardiopathy and peripheral arterial blood vessel
maladies. The preponderance of AF grows with the age and is the familiar arrhythmia inducing medical care and hospitalization.
A normal heart rate should be in between 60- 100 beats per minute in the resting state of a person [1]. The heart rate can be
measured by feeling pulses and counting in the neck or wrist. In AF, the rate of heart would increase over 140 beats per minute.
AF is the almost usual cardiopathy of cardiac arrhythmia encountered in clinical exercises. About 1/3 of the hospital entrances for
admissions in respect of heart beat irregular disorders is due to AF [3]. AF can exist in the arrangement of a broad form of cardiac
and non-cardiac circumstances, it is in most cases symptomless having undefined not particular evidences. In several cases, after
major serious problems such as stroke, heart failure or thromboembolism the AF is discovered in a patient. The initial medical
diagnosis of AF relies upon relating symptoms such as dyspnea, shortness of breath, palpitations, and dizziness/syncope or chest
uneasiness with AF. Manual heart beat scrutiny should be executed to evaluate for the existence of an arrhythmia heart beat that
may point fundamental AF [2]. AF has one of the most complications is heart stroke, figured that approx. 15% ischemic stroke is
due to it. The people having AF have a higher risk of heart stroke of 3-5 times more than those not having AF.
Atrial fibrillation may be classified in several manners, depending on the level to which it strikes a person:
TABLE 1
Classification system
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Rajan Chaudhary and Santosh Kumar
AF Category
Defining Characteristics
First detected
only one diagnosed episode
Paroxysmal
recurrent episodes that self-terminate in less than 7 days.
Persistent
recurrent episodes that last more than 7 days
Permanent
an ongoing long-term episode
As shown in Table 1, AF is classified in four categories [3] whose characteristics is defined.
II.
DESCRIPTION OF ATRIAL FIBRILLATION
In different methods of diagnosis for Atrial fibrillation an electrocardiogram (ECG) is the common method. The electrocardiogram
is a test that assesses the cardiovascular electrical activity. The heart is a muscular body organ that pulses in heart beat rhythm to
supply the blood in circulation throughout the parts of the body. The ECG is a device which is utilized in the detection, recording
and diagnosis of abnormalities in the heart that assesses electrical potentials on the skin surface through electrodes and yields an
account of the electric current events affiliated with cardiovascular activities. ECG is a noninvasive process for registering
cardiovascular electrical activity changes. Here graphs of Normal sinus rhythm and AF [6] are shown and comparison of AF with
Normal sinus rhythm is done and described.
A. Normal Sinus Rhythm Graph- Having heart beat rate of 95 per minute here:
Figure 1. Normal sinus rhythm graph for 1 minute data
B. AF Graph- Having heart beat rate of 107 per minute here:
Figure 2. AF graph for 1 minute data.
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Comparison on Time Basis of Atrial Fibrillation with Normal Sinus Rhythm ECG on MATLAB
Figure 3. ECG of atrial fibrillation (top) and normal sinus rhythm (bottom). The purple arrow indicates a P wave, which is lost in atrial fibrillation
In Fig. 1 and 2 signal wave of Atrial Fibrillation is differentiated with the Normal Sinus Rhythm signal of a healthy person. In Fig.3
clear comparison of both the ECG are done [3]. Here it is clearly illustrated that P waves are absent [5] in the characteristic findings,
with unsystematic electrical activity in their position, and due to asymmetric conduction of impulses to the ventricles there is
asymmetric R-R intervals.
Atrial Fibrillation (AF) of heart is an atrial tachyarrhythmia distinguished by preponderantly unorganized atrial commencement with
subsequent worsening of atrial mechanical operations. Atrial Flutter is another intimately corresponding arrhythmia as shown in Fig.
4. Atrial flutter is a state where there are multiple atrial compressions for all ventricular contraction. It is induced by an individual
wide electrical signal that spreads around the atria. The order of atrial contraction can be between 200 and 350 beats a minute. The
quantity of blood being supplied by the atria can be very little as an outcome of one face of the atria being compressed while the
other being relaxed. The electrical signals get into the AV node at an order that is too fast to produce a ventricular compression for
all atrial compression. As an outcome, there are multiples P-waves in the ECG for all QRS-T complex.
Figure 4. Atrial flutter
On the ECG while measurement, AF is depicted by the ordered P waves absence; rather there are fast fibrillatory waves or
oscillations that alter in shape, size and timing and are normally related with an asymmetric ventricular effect when atrioventricular
(AV) conduction is inviolate. An asymmetric, maintained, fast, broad QRS complex tachycardia could advice for AF with
conduction through an accessory pathway (an abnormal conduction circuit within the heart leading to asymmetric heart rhythms).
III.
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HEART ACTIVITIES HAVING AF
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Heart is the most important organ of the body and its principal function is to circulate, distribute and provide nutrients and blood to
the body. The circulation and transferring of the blood throughout the body is due to the appropriate beating or compression of the
heart. Electrical impulses travelling through the heart controls the heart beats. In the normal heart these impulses are in regular
interval of time. Otherwise in abnormal case irregular rhythms takes place, if there is some failure in the electrical system.
Figure 5. The heart’s electrical conduction system
In the Fig.5 The heart’s electrical conduction system is shown, in which Sino-atrial (SA) node is at the top of the right atria is the
heart’s ‘natural’ pacemaker and consists of a set of particular cardiac cells. An electric impulse originated in the SA node is
produced by the interchange of electrical ions throughout the cardiac cells or myocytes. The compression and release of blood into
the ventricles is due to the rhythmic wave of the electrical activity throughout the atria, due to impulse. Discharge of the blood into
the ventricles is due to the pulses of activity throughout the atria causing the compression.
Figure 6. Disturbed electrical conduction in a heart with atrial fibrillation.
As in Fig.6 it is shown that AF arises due to an irregularity of the electrical signaling pathway. Rather signals succeeding a proper
coordinated pathway, signals are not consistently sparked through the SA node, but rather, are produced throughout the atria,
leading to a palpitation or fibrillation unorganized atrial operations.
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Comparison on Time Basis of Atrial Fibrillation with Normal Sinus Rhythm ECG on MATLAB
Figure 7. Generation sites of multiple electrical impulses in atrial fibrillation
As shown in Fig. 7, in almost cases of AF, inside the left atrium, the space across the pulmonary veins seems to be the place where
multiple impulses are produced. About 300 to 600 beats a minute can be raised by the impulses produced disabling AV node to filter
the amount of signals from atria because of too rapid, disordered and asymmetric natured signals from multiple areas of atria,
leading to poor evacuation of the atria. This will cause about 100 or more beats a minute, striking the evacuation of the ventricles. If
this condition continues then it will lead to reduction in circulation of blood and hence cause of AF and symptoms.
Figure 8. Clot formation in an atrial appendage
As in Fig. 8 atrial appendage is shown, it is inside the left atrium of the heart area. And is an enlarged atrial muscle volume that
builds a tiny pouch or sac. Due to fibrillation, atria are unable to evacuate blood properly while AF, which admits ‘pooling’ of blood
inside the atrial appendage. Stagnation of blood takes place in this pouch leading to clot constitution. As the clot part breaks away,
an embolus is made, a cause for embolic stroke. A characteristic of AF is that, as an outcome of improper evacuation, the atria
increases and causing inclining element to everlasting AF in adults [7].
IV.
i.
PREVENTIONS AND TREATMENTS FOR AF
PREVENTION: To prevent reoccurrence of AF [8]:
• Minimize the consumption of drinks and products consisting caffeine.
• Look out for some OTC (over-the-counter) medications, such as cold medicines containing pseudoephedrine, as they can
trigger AF.
• Some OTC medications interact with anti-arrhythmic medications. So look out for them. Communicate with the doctor.
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• Omega-3 fatty acids have been accounted to diminish the incidence of few cardiac arrhythmias.
ii.
•
•
•
•
TREATMENTS: Treatments that may be considered include [9]:
Rate control- This intends getting the heart rate draw back to normal. This is practiced for everyone with AF who have a
rapid heart rate (that is, most cases).
Rhythm control- This intends drawing the asymmetric rhythm back to a normal symmetric rhythm. This is exclusively
executable in few cases.
Anticoagulation treatment which targets to avoid a stroke.
Other treatments in certain conditions.
TABLE 2
Indications for rate or rhythm control in persistent AF [2]
Rate-control strategy
Rhythm-control strategy
Preferred initial option for:
Preferred initial option for:
• older people (>65)
• younger people (<65
• people with coronary artery disease
• people with unacceptable arrhythmia-related
symptoms
• people with contraindications to
• people presenting for the first time with
anti-arrhythmic drugs
lone AF
• those unsuitable for cardioversion
• AF secondary to a treated/corrected
precipitant
• people without congestive heart failure
• people with congestive heart failure
V.
RESULT
In this paper, we compared the amplitude of Normal ECG signal and AF ECG signal on MATLAB [4] which tells us about the
amplitude of Normal ECG signal and AF ECG signal. This work is utilized for the categorization of AF signal through Matlab.
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Comparison on Time Basis of Atrial Fibrillation with Normal Sinus Rhythm ECG on MATLAB
NORMAL SINUS RHYTHM Data
3
2.75
2.5
ECG (mV)
2
1.5
1
0.5
0
-0.5
-1
0
50
100
150
200
250
300
350
400
450
500
550
600
TIME (mS)
Figure 7a. Normal ECG signal
ATRIAL FIBRILLATION Data
1.5
1.25
ECG(mV)
1
0.5
0
-0.5
0
50
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200
TIME (mS)
Figure 7b. Atrial Fibrillation ECG signal
Figure 7. Comparison of Normal (Sinus) with Atrial Fibrillation ECG signal.
From the Fig. 7, in Fig. 7b the QRS peak is very smaller than Fig.7a. So this thing shows that the ECG signal of AF that is Fig.7b is
very different from the Fig. 7a, that is Normal. The amplitude of R wave in Normal ECG signal is 0.2675 mV at 0.422 mSec and
amplitude of AF ECG signal is 1.16 mV at 0.548mSec this thing shows that Fig.7b is different from normal ECG signal and have a
trend of peak length. Red circles are the number of samples. Here 95 peaks a minute for Normal and 107 peaks a minute for AF
ECG signal is applied. Here a specific trend of time gap and peak length is shown at Matlab. We are illustrating the changing trends
of signal’s pattern in the initial time (5 sec.) of the signals movement [6].
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TABLE 3
COMPARISION OF DIFFERENT ECG SIGNALS
Heart Rate
NORMAL
ATRIAL FIBRILLATION
Peak No.
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Sampled at
55
132
210
287
366
445
524
138
277
418
559
698
836
977
Time (in mSec)
0.422
1.023
1.633
2.234
2.852
3.469
4.086
0.548
1.104
1.668
2.232
2.788
3.34
3.904
ECG (mV)
2.675
2.925
2.855
2.885
2.615
2.655
2.795
1.16
1.13
1.155
1.275
1.225
1.2
1.39
In Table 3, conditions of different ECG signals are compared. Data [6] is for one minute, peak value (R) of signal is in mV at Time in mSec, whose
value of sample is shown. As it is clearly compared the difference, the first peak of Normal is at 0.422 mSec, AF is at 0.548 mSec; for the next
peak it is 1.023 mS for Normal and for AF it is 1.104 mS sampled at (just to show the exact location of peak) 277, hence in AF comparatively time
gap between two peaks is lesser than Normal, showing more peaks per minute. So from this table we established a trend of average time gap
between two peaks on Matlab and made calculation plus categorization very fast .Similarly it can be easily comparable for other peak values.
VI. CONCLUSIONS
The paper presents an original work related to comparison and categorization of AF ECG signal. An excellent analysis of AF
problem and ECG signal is exposed.
Matlab programming environment is used for all the simulations. It is the extraordinary platform to evaluate large number of data
and giving accurate, reliable and faster calculations. It functions good to sustain the archetype signal and has crucial practical value.
The reference ECG signal were generated using a predefined genuine source. Some outcomes and details are clearly exposed. The
significance of this scientific research might be very useful for explorers and inventors in the field of ECG signal processing.
In this work, a robust technique of understanding vulnerable AF ECG signals and its categorization is stated. This novel technique
can be accustomed to recognize other heart disorders also.
REFERENCES
[1] Rajan Chaudhary, Anand Prakash, Chandan Gupta, “Comparison of Different ECG Signals on Matlab”. International Journal of Electronics and Computer
Science Engineering. Vol.2, No.2, May 2013. PP-733-739.
[2] National Collaborating Centre for Chronic Conditions. Atrial Fibrillation: National clinical guideline for management in primary and secondary care. London:
Royal College of Physicians; 2006.
[3] Fuster V, Rydén LE, Cannom DS et al. (2006). "ACC/AHA/ESC 2006 Guidelines for the Management of Patients with Atrial Fibrillation: a report of the
American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice
Guidelines (Writing Committee to Revise the 2001 Guidelines for the Management of Patients With Atrial Fibrillation): developed in collaboration with the
European Heart Rhythm Association and the Heart Rhythm Society". Circulation 114 (7): e257–354.
[4] Gari D. Clifford, Francisco Azuaje and Patrick McSharry, “Advanced methods & tools for ECG Data Analysis” Artech House Publishers -Sep 2006.
[5] B.R. Shankara Reddy, Paul E. Elko, Dave W. Christenson, and G. Ian Rowlandson,” Detection of P Waves in Resting ECG: A Preliminary Study”, IEEE, 1992.
[6] http://www.physionet.org
[7] http://www.educationforhealth.org
[8] http://www.medicalnewstoday.com/info/atrial-fibrillation/complications-of-atrial-fibrillation.php
[9] http://www.patient.co.uk/health/atrial-fibrillation
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