Identification of Coronary Artery Diseased Subjects Using Spectral Featuries

Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity globally. Nowadays, it is spreading at an alarming rate. Recently, there is an increasing interest to develop simple and non-invasive automated methods for reliable diagnosis of CAD. Studies reported that the use of single-channel phonocardiogram (PCG) signal for detecting weak CAD murmurs caused by the stenosed coronary arteries due to turbulent blood flow. In this work, we introduce a new framework with multi-channel data acquisition system to classify CAD and normal subjects. The proposed method does not require any reference signal such as an electrocardiogram (ECG) signal for PCG signal segmentation as reported in the earlier studies. Subsequently, the study has used five different features, such as spectral moments, spectral entropy, moments of PSD function, autoregressive (AR) parameters, and instantaneous frequency derived from frequency representations of PCG signals. These features have captured the specific details related to the disease. We use an artificial neural network (ANN) for the classification task. Experimental results show that the AR features well-performed. We achieve an accuracy of 74.24% by using multi-channel recorded data where as the best performance obtained using single-channel signal is 69.69%.