Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling

TL;DR

This paper introduces a deep ensemble model for multi-label classification of heart sounds, enabling detailed annotation of various cardiac features, which improves diagnostic accuracy for cardiovascular disorders.

Contribution

The work presents a novel deep multilabel learning approach for heart sound analysis, extending beyond single-label classification to multi-label annotation of multiple cardiac sound features.

Findings

Achieved sensitivity of 0.990 and specificity of 0.999 in segment classification.

Attained an overall accuracy of 0.969 at the patient level.

Demonstrated superior performance over existing methods.

Abstract

Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels from different label groups, including murmur's timing, pitch, grading, quality, and shape. Our experiment results show that the proposed method has achieved outstanding performance on the holdout data for the multi-labelling task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level, and an overall accuracy=0.969 at the patient's recording level.