Diagnosis of Coronary Artery Disease Using Deep Belief Networks

Year 2017, Volume: 2 Issue: 1, 29 - 36, 25.02.2017

Gokhan Altan

Abstract

In
this study, a decision-support system is
presented to aid cardiologists during the diagnosis and to create a base for a
new diagnosis system which separates two classes (CAD and no-CAD patients)
using an electrocardiogram (ECG).

24 hour filtered ECG signals from PhysioNet
were used. 15 second short-term ECG segments were extracted from 24 hour ECG
signals to increase the number of samples and to provide a
convenient transformation in a short period of time. The Hilbert-Huang
Transform, which is effective on non-linear and non-stationary signals, was
used to extract the features from short-term ECG signals. Instinct Mode
Function (IMF) was extracted by applying Empirical Mode Decomposition to
short-term ECG signals. The Hilbert Transform (HT) was applied to each IMF to obtain
instantaneous frequency characteristics of the signal. Dataset was created by
extracting statistical features from HT applied to IMF. Deep Belief Networks
(DBN) which have a common use in Deep Learning algorithms were used as the
classifier. DBN classification accuracy in the diagnosis of the CAD is
discussed. The extracted dataset was tested using the 10-fold cross validation
method.

The test characteristics (sensitivity,
accuracy and specificity) that are the basic parameters of independent testing
in the medical diagnostic systems were calculated using this validation method.
Short-term ECG signals of CAD patients and no-CAD groups were classified by the
DBN with the rates of 98.05%, 98.88% and 96.02%, for accuracy, specificity and
sensitivity, respectively.

The DBN
model achieved higher accuracy rates than the Neural Network classifier.

Keywords

Coronary Artery Disease, CAD, Deep Belief Networks, DBN, Deep Learning Algorithm, Hilbert-Huang Transform

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