An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems still confronted with a problem reduces their efficiency, such as energy consumption in wearable devices because they battery-powered and have limited storage. This paper presents novel algorithm compression to reduce RHMs. The proposed uses discrete Krawtchouk moments feature extractor obtain features from signal. Then accelerated Ant Lion Optimizer (AALO) selects optimum achieve best-reconstructed Our is extensively validated using two benchmark datasets: MIT-BIH arrhythmia ECG-ID. provides average values ratio (CR), percent root mean square difference (PRD), signal noise (SNR), Peak Signal (PSNR), quality score (QS) 15.56, 0.69, 44.52, 49.04 23.92, respectively. comparison demonstrates advantages on recent algorithms concerning mentioned performance metrics. It also tested compared against other existing Processing Time, speed computational efficiency. obtained results show extremely outperforms terms (Processing Time = 6.89 s), (compression 4640.19 bps) (computational efficiency 2.95). indicate device by decreasing wake-up time 3600 ms.