Elgendi Mohamed, Al-Ali Abdulla, Mohamed Amr, Ward Rabab
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V6H 3N1, Canada.
Diagnostics (Basel). 2018 Jan 16;8(1):10. doi: 10.3390/diagnostics8010010.
Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
移动技术的最新进展促使人们在远程监测环境和智能家居中转向使用电池供电设备。临床医生正在根据为需要持续监测的门诊患者远程收集的心电图（ECG）信号开展诊断和筛查程序。对大量记录的ECG信号进行高速传输和分析至关重要，尤其是在电池供电设备使用增加的情况下。需要探索具有高效率的低功耗替代压缩方法，以实现智能家居或远程位置的ECG信号采集、传输和分析。基于自适应线性预测器和以B / K为因子的抽取的压缩算法，根据压缩率（CR）、均方根差百分比（PRD）以及重建ECG信号的心跳检测准确率进行评估。在两个数据库（153名受试者）中，新算法在两个数据库上均展现出最高的压缩性能（CR = 6且PRD = 1.88）和总体检测准确率（灵敏度99.90%，阳性预测值99.56%）。所提出的算法通过更快、更高效的方法为ECG信号的实时传输提供了优势，满足了对更高效远程健康监测日益增长的需求。
