A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection

Valve sounds are mostly a result of heart valves opening and closing. Laminar blood flow is interrupted and abruptly transforms into turbulent flow, causing some sounds, and is explained by improper valve operation. It has been feasible to demonstrate that the typical and compulsive instances are different for both chronological and spatial aspects through the examination of phono-cardiographic signals. The current work presents the development and application of deep convolutional neural networks for the binary and multiclass categorization of multiple prevalent valve diseases and typical valve sounds. Three alternative methods were taken into consideration for feature extraction: mel-frequency cepstral coefficients and discrete wavelet transform. The precision of both models accomplished F1 scores of more than 98.2% and specificities of more than 98.5%, which reflects the instances that can be wrongly classified as regular. These experimental results prove the proposed model as a highly accurate assisted diagnosis model.

Automated summary

This paper introduces the application of deep convolutional neural networks in the diagnosis of heart valve diseases. The binary and multiclass categorization of valve diseases and normal heart sounds is achieved through audio signal processing. Both feature extraction methods achieved precision scores of over 98.2%, indicating that the proposed model is a highly accurate assisted diagnosis model.