Deep learning based cardiovascular disease diagnosis system from heartbeat sound

During each cardiac cycle of heart, vibrations creates sound and murmur. When these sound and murmur wave is represented graphically then it is called phonocardiogram (PCG). Digital stethoscope is used to record the audio wave signals generated due to heart vibration. Audio waves recorded through digital stethoscope can be used to fetch information like tone, quality, intensity, frequency, heart rate etc. Based on the heart condition, this information will be different for different people and can be used to predict the status of heart at early stage in non-invasive manner. In this research work, by using deep learning models, authors have classified PCG signals into 5 classes namely extra systole, extra heart sound, artifacts, normal heartbeat and murmur. Initially spectrograms in the form of images are extracted from PCG sound and feed into Regularized Convolutional Neural Network. From the simulation environment designed in python, it has found that proposed model has shown the average accuracy of 94% while doing the classification of PCG sound in five classes.