基于块的 Haar 小波变换和 COVIDOA 对物联网医疗系统生物信号的压缩

Compression of Bio-Signals Using Block-Based Haar Wavelet Transform and COVIDOA for IoMT Systems.

作者信息

Khafaga Doaa Sami, Aldakheel Eman Abdullah, Khalid Asmaa M, Hamza Hanaa M, Hosny Khaid M

机构信息

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Information Technology Department, Faculty of Computers & Informatics, Zagazig University, Zagazig 44519, Egypt.

出版信息

Bioengineering (Basel). 2023 Mar 24;10(4):406. doi: 10.3390/bioengineering10040406.

DOI:10.3390/bioengineering10040406

PMID:37106593

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10135695/

Abstract

BACKGROUND

Bio-signals are the essential data that smart healthcare systems require for diagnosing and treating common diseases. However, the amount of these signals that need to be processed and analyzed by healthcare systems is huge. Dealing with such a vast amount of data presents difficulties, such as the need for high storage and transmission capabilities. In addition, retaining the most useful clinical information in the input signal is essential while applying compression.

METHODS

This paper proposes an algorithm for the efficient compression of bio-signals for IoMT applications. This algorithm extracts the features of the input signal using block-based HWT and then selects the most important features for reconstruction using the novel COVIDOA.

RESULTS

We utilized two different public datasets for evaluation: MIT-BIH arrhythmia and EEG Motor Movement/Imagery, for ECG and EEG signals, respectively. The proposed algorithm's average values for CR, PRD, NCC, and QS are 18.06, 0.2470, 0.9467, and 85.366 for ECG signals and 12.6668, 0.4014, 0.9187, and 32.4809 for EEG signals. Further, the proposed algorithm shows its efficiency over other existing techniques regarding processing time.

CONCLUSIONS

Experiments show that the proposed method successfully achieved a high CR while maintaining an excellent level of signal reconstruction in addition to its reduced processing time compared with the existing techniques.

摘要

背景

生物信号是智能医疗系统诊断和治疗常见疾病所需的关键数据。然而，医疗系统需要处理和分析的此类信号数量巨大。处理如此大量的数据存在困难，例如需要高存储和传输能力。此外，在应用压缩时，在输入信号中保留最有用的临床信息至关重要。

方法

本文提出了一种用于物联网医疗应用中生物信号高效压缩的算法。该算法使用基于块的小波变换（HWT）提取输入信号的特征，然后使用新颖的COVIDOA选择最重要的特征进行重建。

结果

我们分别利用两个不同的公共数据集进行评估：用于心电图（ECG）信号的麻省理工学院 - 比赫心律失常数据库（MIT - BIH arrhythmia）和用于脑电图（EEG）信号的脑电图运动运动/想象数据库（EEG Motor Movement/Imagery）。对于ECG信号，所提出算法的压缩率（CR）、百分比根均方误差（PRD）、归一化互相关系数（NCC）和质量得分（QS）的平均值分别为18.06、0.2470、0.9467和85.366；对于EEG信号，分别为12.6668、0.4014、0.9187和32.4809。此外，在所提出算法在处理时间方面比其他现有技术更具效率。