Detecting abnormal heart sound using mobile phones and on-device IConNet

TL;DR

This paper introduces IConNet, a lightweight, interpretable neural network that enables mobile phones to detect abnormal heart sounds directly from audio recordings, promoting accessible early cardiovascular disease screening.

Contribution

The development of IConNet, a novel on-device neural network architecture that analyzes raw audio signals for abnormal heart sound detection without specialized equipment.

Findings

Achieved accurate detection of abnormal heart sounds on mobile devices.

Enhanced transparency and interpretability of neural network decisions.

Demonstrated potential for remote health monitoring and early diagnosis.

Abstract

Given the global prevalence of cardiovascular diseases, there is a pressing need for easily accessible early screening methods. Typically, this requires medical practitioners to investigate heart auscultations for irregular sounds, followed by echocardiography and electrocardiography tests. To democratize early diagnosis, we present a user-friendly solution for abnormal heart sound detection, utilizing mobile phones and a lightweight neural network optimized for on-device inference. Unlike previous approaches reliant on specialized stethoscopes, our method directly analyzes audio recordings, facilitated by a novel architecture known as IConNet. IConNet, an Interpretable Convolutional Neural Network, harnesses insights from audio signal processing, enhancing efficiency and providing transparency in neural pattern extraction from raw waveform signals. This is a significant step towards trustworthy AI in healthcare, aiding in remote health monitoring efforts.