Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model

Heart sounds have been widely used for years to monitor and classify heart diseases. Experts manually examine these sounds, which is arduous and time-consuming. In addition, since interpreting these sounds requires experience, experts who do not have enough experience may misinterpret these sounds. For this reason, a new deep one-dimensional Convolutional Neural Network (1D-CNN) architecture has been proposed to increase the detection accuracy and alleviate the workload of experts in the classification of sound signals used in the diagnosis of heart valve diseases. In the developed model, first, feature maps were obtained from heart sounds by using the MFCC method. High performance was achieved when the feature maps obtained later were classified in the developed deep architecture. Furthermore, the feature maps generated by the MFCC approach were classified using traditional machine learning classifiers. When the obtained results were compared, it was observed that the suggested deep model was more successful. In the developed architecture, an accuracy rate of 99.5% was obtained. The accuracy rate obtained shows that the developed architecture can be used to classify heart sounds and diagnose heart valve diseases.

Automated summary

This article introduces a new deep convolutional neural network architecture for the classification and diagnosis of heart valve diseases based on heart sounds. By using the MFCC method to obtain feature maps from heart sounds and classifying them in the developed deep architecture, high accuracy rates were achieved.