Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications

Ying-Ren Chien,Kai-Chieh Hsu,Hen-Wai Tsao

doi:10.3390/app10175842

Ying-Ren Chien, Kai-Chieh Hsu + Show 1 more

Open Access

https://doi.org/10.3390/app10175842

Copy DOI

Abstract

Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%.

Highlights

According to the American heart association report, cardiovascular disease (CVD) is the leading global cause of death and it is expected to have more than 22.2 million deaths by 2030 [1]
We focus on the compression of PCG signals by using the deep convolutional autoencoder
The percent root-mean-square difference (PRD) corresponding to compress ratio (CR) = 36 is less than the target PRD (5%), if we further considered the fixed-point computation issue, the resulting PRD

Summary

Introduction

According to the American heart association report, cardiovascular disease (CVD) is the leading global cause of death and it is expected to have more than 22.2 million deaths by 2030 [1]. Auscultation is one of the significant methods for CVD’s monitoring [2]. Heart sound can be used to diagnose heart diseases or to evaluate a human’s physiological condition [3,4]. The electronic stethoscopes exploit the vibrations that are caused by the heartbeats to graphically record the heart sounds called phonocardiography (PCG) signals [5]. The PCG signals provide a non-invasive method for detecting heart valve abnormalities and assisting in diagnosing heart disease. An efficient signal compression method is necessary due to the vast amounts of data that are generated by long-term PCG monitoring

Methods

Results

Discussion

Conclusion