文章摘要

文章信息/Info

Author(s):: LIN Yujie; WANG Xingyao; CHEN Chao; LI Jianqing; LIU Chengyu; （School of Instrument Science and Engineering, State Key Laboratory of Digital Medical Engineering, Southeast University, Nanjing 210096, China）

Keywords:: Electrocardiograph; ECG compression; Adaptive parameter; Wearable ECG

摘要:: 为探究一种适应于临床诊断的高压缩比的心电(electrocardiograph,ECG)压缩方法，本研究提出了一个自适应压缩参数寻优器，基于压缩算法定位出压缩性能最佳的ECG信号参数组。针对算法的普适性,本研究推荐了一组适用于所有ECG信号的参数组，并利用4个指标在MIT-BIH数据库上对压缩性能进行估计。实验结果表明，平均压缩比（compression ratio, CR）达到了26.67，平均百分比均方根误差（percentage root-mean-square difference, PRD）达到了14.64%，压缩一条30 min ECG信号的平均时长为0.125 8 s。本研究改进后的压缩算法在压缩比上表现突出，对临床诊断有应用意义。

Abstract:: In order to explore an electrocardiograph（ECG） compression method available for clinical diagnosis with high compression ratio, the adaptive parameter optimal searcher was proposed to locate the parameters with good compression performance for ECG compression. A set of parameters applicable to all ECG signals was recommended for the universality of the algorithm，and the compression performance was estimated by four indicators on the MIT-BIH database. The experimental results showed that the average compression ratio（CR）was 26.67, the average percentage root-mean-square difference（PRD）was 14.64%, and the average compression time of 30 min ECG was 0.125 8 s. The results elucidate that the improved compression algorithm has outstanding performance in compression ratio as well as application significance in clinical diagnosis.