Segmentation-free Heart Pathology Detection Using Deep Learning

TL;DR

This paper introduces a segmentation-free deep learning approach for heart sound classification that improves accuracy in detecting normal and murmur classes, even under user-independent conditions, without relying on traditional segmentation methods.

Contribution

The study presents a novel segmentation-free method combining wavelet denoising, feature extraction, and machine learning for heart sound classification, outperforming previous segmentation-dependent approaches.

Findings

Achieved 81% and 96% precision on normal and murmur classes.

Demonstrated 92% and 86% precision in user-independent settings.

Outperformed existing methods on the PASCAL dataset.

Abstract

Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a patient based on their heart sounds is a rather difficult skill to master. Thus, many approaches for automated heart auscultation have been explored. However, most of the previously proposed methods involve a segmentation step, the performance of which drops significantly for high pulse rates or noisy signals. In this work, we propose a novel segmentation-free heart sound classification method. Specifically, we apply discrete wavelet transform to denoise the signal, followed by feature extraction and feature reduction. Then, Support Vector Machines and Deep Neural Networks are utilised for classification. On the PASCAL heart sound dataset our approach showed superior performance compared to others, achieving 81% and 96% precision on normal and murmur classes, respectively. In addition, for the first time, the data were further explored under a user-independent setting, where the proposed method achieved 92% and 86% precision on normal and murmur, demonstrating the potential of enabling automatic murmur detection for practical use.