Lightweight electrocardiogram signal compression

TY - JOUR

T1 - Lightweight electrocardiogram signal compression

AU - Kuldeep, Gajraj

AU - Zhang, Qi

N1 - Publisher Copyright: © 2023

PY - 2023/8

Y1 - 2023/8

N2 - Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix's entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

AB - Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix's entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

KW - DCT pruning

KW - E-health

KW - ECG

KW - Lossy compression

KW - Resource-constrained devices

KW - Wearable devices

KW - Wireless body area network

UR - http://www.scopus.com/inward/record.url?scp=85160517380&partnerID=8YFLogxK

U2 - 10.1016/j.bspc.2023.105012

DO - 10.1016/j.bspc.2023.105012

M3 - Journal article

AN - SCOPUS:85160517380

SN - 1746-8094

VL - 85

JO - Biomedical Signal Processing and Control

JF - Biomedical Signal Processing and Control

M1 - 105012

ER -