LAUSR

Abstract Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity. There is a need to develop a simple, reliable, and non-invasive screening tool to diagnose CAD. Prior studies reported that turbulent blood flow due to stenosed coronary arteries causes weak CAD murmurs. Analysis of phonocardiogram (PCG) signals can be useful to detect these murmurs. In this work, we propose a new multi-channel PCG-based system to classify CAD-affected and normal subjects, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal. The proposed system simultaneously acquires PCG signals from four different auscultation sites on the chest. It extracts five different features from time and frequency domains of the PCG signals. The two-class classification is done in a machine learning framework by employing an artificial neural network (ANN) classifier. The classification performances are evaluated for each channel as well as for their combinations. Experimental results show that the proposed sub-band-based spectral features perform well for both clean and noisy data. An accuracy of 82.57% is obtained using the combination of the signals acquired from tricuspid, mitral, and midaxillary regions. The multi-channel system gives more than 4% relative improvement over the best performance obtained by its single-channel counterpart, and the proposed features outperform earlier used features.