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A Comparative Study Of Advanced Signal Processing Techniques For Detection Of Coronary Artery Disease

Previous studies have indicated heart sounds contain information for the detection of partially occluded coronary arteries. In this study, recordings of diastolic heart sound segments were analyzed by using three advanced signal processing techniques; the Autoregressive (AR), the Autoregressive Moving Average (ARMA), and the Minimum-Norm (Eigenvector) methods. In order to enhance the diastolic heart sounds and eliminate background noise, the Adaptive Line Enhancer (ALE) method was used as a preprocessor. These advanced signal processing techniques were used to estimate the model parameters. The poles of the AR, ARMA and Minimum-Norm methods were used to diagnose patients as diseased or normal-Results showed that normal and abnormal records were correctly identified in 32 of 38 (36 patient) cases.