CALIFORNIA STATE UNIVERSITY, NORTHRIDGE ARRHYTHMIA CLASSIFICATION USING ...

36 Pages·2013·0.66 MB·English

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

ARRHYTHMIA CLASSIFICATION USING SUPPORT VECTOR MACHINE

A graduate project submitted in partial fulfillment of the requirements
For the degree of Master of Science
in Electrical Engineering

By

Alexis Pena

August 2013
The graduate project of Alexis Pena is approved:

__________________________________ _________________________
Benjamin F Mallard Date

__________________________________ _________________________
Dr. Ali Amini Date

__________________________________ _________________________
Dr. Xiyi Hang, Chair Date

California State University, Northridge
ii
TABLE OF CONTENTS

Signature page………………………………………………………………………….…ii
Abstract…………………………………………………………………………………...iv
Introduction……………………………………………………………………………..…1
Support Vector Machine ………………………………………………………………….8
Feature extraction ……………………...……………….……………………………......12
Numerical Experiment...……………………………………………….………………...14
Discussion.…………………………………………………………………………….....22
Bibliography .…………………………………………………………………………....23
Appendix…..……………………………………………………………………………..24

iii
ABSTRACT
ARRHYTHMIA CLASSIFICATION USING SUPPORT VECTOR MACHINE
By
Alexis Pena
Master of Science in Electrical Engineering
The number one cause of death in the United States is heart disease (1). 67% of heart
disease death is brought on by sudden cardiac death. Most of these cases are due to an
abnormal electrical triggering of the heart which disrupts blood circulation that lead to
immediate death if electrical shock therapy is not applied. Automatic arrhythmia analysis
helps speed up the defibrillation therapy to correct the disruption in the electrical system.
Implantable defibrillators analyze every heart beat and proper classification is the key to
saving thousands of lives every year. In my project, I approached automatic arrhythmia
analysis through support vector machine classification (SVMs). I analyze time series
ECG heartbeat files from multiple subjects with various arrhythmias, as well as normal
heartbeats with no arrhythmias. The heartbeat signals are extracted into individual beats
and classified using SVM. The performance of the SVM is evaluate through multiple
kernel modification, including higher order dimension analysis and compared to achieve
the best classification accuracy rate. Finally, the wavelet transform is taken of the
individual signals to maintain enough features while compressing and reducing the
sample size to classify each signal with a high accuracy rate. Through my numerical
experiments I conclude that SVM provides fast and accurate arrhythmia classification
allowing automatic analysis without human oversight.
iv
INTRODUCTION
A healthy human heart pumps about 2,000 gallons of blood through all of our veins and
arteries every single day. It carries oxygen and nutrients to every cell in our body. With
an average of 100,000 heart beats per day, (2) it is one of the hardest working organs in a
human body. The heart beats because of the small electrical current generated by the
cardiac conduction system. An electrocardiogram, ECG is a noninvasive procedure that
records the electrical activity of the heart over a period of time. The morphology of a
heartbeat can provide many detail of a patient. We can determine the cause of symptoms
of heart disease, such as dizziness, fainting or shortness of breath. We can check the
health of the heart when other conditions are present, such as diabetes, high cholesterol,
high blood pressure, or cigarette smoking. And we can check the maintenance of
mechanical devices, such as a pacemaker working in combination with other organs
throughout the rest of the body. An ideal heart beat has five deflections.

Figure 1. QRS complex wave with P and T wave.
The ECG will record the electrical activity that comes from depolarization and
repolarization of the heart muscle cells when the atria and ventricles contract. The P wave
results from atrial depolarization which spreads from the SA node throughout the atria
and pumps blood through the open valves from the atria into both ventricles. The time
1
between the P and Q wave is when the signal arrives at the atrioventricular (AV) node
and allows the heart’s left and right ventricles to fill with blood. The Q wave results from
when the signal arrives at the bundle of His and is divided into left and right bundle
branches. The signal leaves the bundle branches through the Purkinje fibers and spreads
across the heart’s ventricles causing them to contract which pushes the blood though the
pulmonary value into the lungs and through the aortic valve to the rest of the body . The
left ventricles contracts first immediately followed by the right, this is marked by the R
wave and S wave respectively. After the signal passes, the ventricles relax and await the
next signal. The T wave marks the point when the ventricles are relaxing.
Any changes from a normal heart beat may indicate a problem with the patient. A patient
with atrial premature complexes, APC will have a reduced p wave. In this paper, the
following arrhythmia groups have been considered: Normal Sinus Rhythm (NOR), Atrial
Premature Contractions (APC), Ventricular premature complexes (VPC), Left Bundle
Branch Block (LBBB) and Right Bundle Branch Block (RBBB). An atrial premature
contraction (APC) is a heart rhythm disorder that involves a premature firing of the
atrium (3), its feels like an extra heartbeat. On an ECG, APC will have a reduce P wave.
2
Figure 2. ECG of a heart beat with APC
A ventricular premature complex (VPC) is perceived as a skipped beat, with a sensation
feeling of palpitations in the chest. On an ECG, VPC is identifiable when the P wave is
closer to the QRS complex. You will also see a longer duration from the T wave to the
next QRS complex (4).

Figure 3. ECG of a heart beat with VPC
3
In Left bundle branch block, (LBBB) the left ventricle contracts later than the right
ventricle. On an ECG, the T wave is deflected opposite compared to a normal sinus
rhythm.

Figure 4. ECG of a heart beat with LBBB
Right bundle branch block, (RBBB) is the result when the right ventricle is not activated
by the current charge traveling through the right bundle branch. During this time, the left
ventricle is operating normal. On an ECG graph we can see a slurred S wave as well as a
widen QRS complex (5).
4
Figure 5. ECG graph showing a heartbeat with RBBB

In a normal heart beat we can see the P wave, QRS complex and the S wave.

Figure 6. ECG of a normal heart beat.
Comparing the 4 arrhythmias to the normal heart beat, we can clearly distinguish the
features that give the ECG its unique characteristics for each arrhythmia. A trained
cardiologist can diagnose various arrhythmias just by looking at the ECG of a patient.
This method is not very practical since the cardiologist cannot monitor the patient at all
5
times. A holter monitor is used to record the heart’s activities for at least 24 hours and
later analyzed. In 24 hours, the holter monitor will record about 100,000 heartbeats
making it a tedious task to do without computer help. The computational analysis of the
ECG signals gives an output with a tally of the unique recorded heart beats. The field of
arrhythmia classification is continuously evolving. There are several techniques for
providing classification. Shape based matching algorithms using Fourier transform are
used for classification from ECG printouts (6). In pattern recognition, k-nearest neighbor
and neural networks provide similar approaches to that of SVM. Morphological pattern
recognition is the leading method for accurate classification among ECG signals. I
propose using the integrated software for support vector classification, libsvm to do
multiclass classification with a multi-order polynomial kernel to provide a high accuracy
rate of 99.6% in arrhythmia classification. I take advantage of SVM’s simplicity.
Through minimal code and lack of computational expenses, I incorporated arrhythmia
analysis that could be used in implantable devices to save thousands of lives each year. In
this paper I explain the mathematical background and methods of support vector machine
and feature extractions to bring an understanding of classification. I explain the method
of gaining the ECG signals and how training and testing of the datasets was completed
using matlab. In chapter 5, procedure i go into detail of how each heartbeat was
separated, grouped and label into a single matrix file. A 10-fold cross validation was
performed to achieve the best kernel function for a high accuracy rate within
classification. A feature extraction process was performed with wavelet transform in
matlab to reduce the signal data while maintaining enough features to perform
classification without a drop in accuracy. In the proceeding chapter, I clarify the results of
6