Phonocardiography signals compression with deep convolutional autoencoder for telecare applications
Journal
Applied Sciences (Switzerland)
Journal Volume
10
Journal Issue
17
Date Issued
2020
Author(s)
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%. © 2020 by the authors.
Subjects
Autoencoder; Deep learning; One-dimensional convolutional neural network (1D CNN); Phonocardiogram (PCG); Signal compression; Telecare
SDGs
Type
journal article