Lightweight electrocardiogram signal compression

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.