Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

Abstract

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual Geometry Group (VGG16), ResNet, DenseNet, and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and abnormal. For PhysioNet 2016, DenseNet has outperformed its peer models with an accuracy of 89.04 percent, whereas for PASCAL 2011, VGG has outperformed its peer approaches with an accuracy of 92.96 percent.