Abnormal heart sound recognition using SVM and LSTM models in real-time mode

Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Normal heart sounds have a special rhythmic pattern as an indicator of heart integrity. Many experts concentrate on diagnosing the heart by automatic digital auscultation systems which find various distinguishable characteristics for heart sound classifications. This can decrease the mortality rate for cardiovascular diseases and enhance the patient's quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. We utilized three datasets, the PhysioNet of 1395, the GitHub of 800, and the PASCAL of 100 files segmented into three cardiac cycles. The proposed solution relies on the support vector machine and the long-short term memory neural network to distinguish between normal and abnormal heartbeat sounds and to recognize the type of abnormality (in the case distinguished) respectively. The results show that the proposed approach for normal/abnormal classification achieves an overall accuracy of 96.0 and 98.1%, sensitivity of 94.4 and 84.2%, and specificity of 64.9 and 98.4% for two and one support vector machines respectively among the state-of-the-art solutions. The long short-term memory model is also a well-known efficient classifier for temporal data, and the results show the accuracy of 99.2, 99.5, 98.6, and 99.4% for four (aortic stenosis (AS), mitral regurgitation (MR), mitral stenosis (MS), and mitral valve prolapse (MVP)), five (AS, MR, MS, MVP, and normal), six (AS, MR, MS, MVP, extrahls, and extrasystole), and seven classes (AS, MR, MS, MVP, extrahls, extrasystole, and normal). Furthermore, we found an efficient automatic segmentation method that was tested with the PASCAL database achieving a total error of 867,525.6 and 23,590.3 ms for datasets A and B respectively, with a computational time of 0.04 s to segment one cardiac cycle.