Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Authors:
Donna Giri;U. Rajendra Acharya;Roshan Joy Martis;S. Vinitha Sree;Teik-Cheng Lim;Thajudin Ahamed, VI;Jasjit S. Suri
Affiliations:
SIM University, School of Science and Technology, Singapore 599491, Singapore;Department of Electronics & Communication Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore and Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Mal ...;Department of Electronics & Communication Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;Global Biomedical Technologies, Inc., Roseville, CA 95661, USA;SIM University, School of Science and Technology, Singapore 599491, Singapore;Department of Electronics & Communication Engineering, Government Engineering College, Wayanad, Kerala 670 644, India;Department of Biomedical Engineering, Idaho State University (Aff.), Idaho, USA
Venue:
Knowledge-Based Systems
Year:
2013

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Abstract

Coronary Artery Disease (CAD) is the narrowing of the blood vessels that supply blood and oxygen to the heart. Electrocardiogram (ECG) is an important cardiac signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insights into the state of health and nature of the disease afflicting the heart. However, it is very difficult to perceive the subtle changes in ECG signals which indicate a particular type of cardiac abnormality. Hence, we have used the heart rate signals from the ECG for the diagnosis of cardiac health. In this work, we propose a methodology for the automatic detection of normal and Coronary Artery Disease conditions using heart rate signals. The heart rate signals are decomposed into frequency sub-bands using Discrete Wavelet Transform (DWT). Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were applied on the set of DWT coefficients extracted from particular sub-bands in order to reduce the data dimension. The selected sets of features were fed into four different classifiers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN) and K-Nearest Neighbor (KNN). Our results showed that the ICA coupled with GMM classifier combination resulted in highest accuracy of 96.8%, sensitivity of 100% and specificity of 93.7% compared to other data reduction techniques (PCA and LDA) and classifiers. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of CAD with higher accuracy.