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

• Published in: IEEE access Vol. 10; pp. 39129 - 39144
• Main Authors: Fathi, Islam S., Makhlouf, Mohamed Abd Allah, Osman, Elsaeed, Ahmed, Mohamed Ali
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant Lion Optimizer; Arrhythmia; Compression algorithms; Compression ratio; Computational efficiency; Computing time; Data compression; Discrete wavelet transforms; Efficiency; electrocardiogram (ECG); Electrocardiography; Encoding; Energy consumption; energy efficiency; Feature extraction; Health care; krawtchouk moments; Medical services; Monitoring systems; Performance measurement; Remote healthcare monitoring; Remote monitoring; Sensors; signal compression; Signal to noise ratio; System effectiveness; Telemedicine; Wearable technology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3166476

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