Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic.
Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00, Brno, Czech Republic.
Sci Rep. 2020 Sep 25;10(1):15801. doi: 10.1038/s41598-020-72656-6.
Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.
ECG 信号的压缩在远程医疗中的信号传输领域尤为重要。有许多压缩算法在不同的细节中被描述，在不同的数据集上进行测试，并且它们的性能以不同的方式表示。在这个领域缺乏标准化。本研究指出了这些缺点，并提出了一种新的压缩算法，该算法被正确地描述、测试，并与其他作者的算法进行了客观比较。本研究为标准化应该是什么样子提供了一个范例。引入了基于单周期分形（SCyF）的压缩算法，并在 4 个不同的数据库（CSE 数据库、MIT-BIH 心律失常数据库、高频信号和布尔诺科技大学心电图质量数据库（BUT QDB））上进行了测试。SCyF 算法总是在效率（2 种方法）和压缩后信号的质量/失真（12 种方法）方面与基于小波变换和分层树集分割的知名算法进行比较。提供了对结果的详细分析。SCyF 压缩算法的结果达到了 avL=0.4460 bps 和 PRDN=2.8236%。
