Heart Anomaly Detection using Deep Learning Approach based on PCG Signal Analysis
Phonocardiography is one of the effective techniques for recording of heart sound during a cardiac cycle which helps in identification and further diagnosis of diseases related to human heart. Contraction of heart muscles and closure of heart valve produces heart sound, which can be analyzed by an experienced cardiologist. The objective of this study is to generate an automatic classification method using one dimensional convolution neural network based on phonocardiogram data for anomaly detection in heart sound. The proposed system consists of three stages namely 1) Data Acquisition 2) Pre-processing 3) Feature Extraction and Classification. We proposed an intelligent neural network approach for classification of PCG data. Heart sound recording (PCG data) which is nothing but an audio file is converted into its time domain representation. This converted PCG data is fed as input to convolution neural network. Emphasis was also given on noisy heart sound recording. Noise can reduce the efficiency of classification as it can disturb the values of neural network. The Accuracy of the proposed system is 91.5% with sensitivity of 0.92 and specificity of 0.91.