Heartbeat Sound Classification Based on Neural Network

The classification of heart sounds is essential for the early detection of cardiovascular disorders. Although heart sound classification has made tremendous strides in recent years, the majority of them still rely on conventional segmentation features and shallow structure-based classifiers. As a result, we suggest a novel method for classifying heart sounds that is based on deep residual learning and enhanced Mel-frequency cestrum coefficient characteristics. The improved features of the heart sound signal are first computed after preprocessing. The neural network uses these features as input features after that. The deep residual network extracts even more pathogenic information from the heart sound stream. The heart sound signal is finally categorized into various groups based on the features that the neural network has learned. In-depth studies of various network parameters and connectivity methods are presented in this research. For the dataset used in this study, the proposed approach obtains an accuracy of 94.43%.