Applying an ensemble convolutional neural network with Savitzky–Golay filter to construct a phonocardiogram prediction model

Abstract

Coronary artery disease is a common chronic disease, also known as ischemic heart disease, which is a cardiac dysfunction caused by the insufficient blood supply to the heart and kills countless people every year. In recent years, coronary artery disease ranks first among the world’s top ten causes of death. Cardiac auscultation is still an important examination for diagnosing heart diseases. Many heart diseases can be diagnosed effectively by auscultation. However, cardiac auscultation relies on the subjective experience of physicians. To provide an objective diagnostic means and assist physicians in the diagnosis of heart sounds at a clinic, this study uses phonocardiograms to build an automatic classification model. This study proposes an automatic classification approach for phonocardiograms using deep learning and ensemble learning with a Savitzky–Golay filter. The experimental results showed that the proposed method is very competitive, and showed that the performance of the phonocardiogram classification model in hold out testing was 86.04% MAcc (86.46% sensitivity, 85.63% specificity), and in ten-fold cross validation it was 89.81% MAcc (91.73% sensitivity, 87.91% specificity). These two experimental results are all better than two state-of-art algorithms and show the potential to apply in real clinic situation.