Khafaga Doaa Sami, Aldakheel Eman Abdullah, Khalid Asmaa M, Hamza Hanaa M, Hosny Khaid M
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.
Bioengineering (Basel). 2023 Mar 24;10(4):406. doi: 10.3390/bioengineering10040406.
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.
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.
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.
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.
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。
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。
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。
实验表明，与现有技术相比，所提出的方法除了处理时间减少外，还成功实现了高压缩率，同时保持了优异的信号重建水平。
