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

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.

Efficient Compression of Fetal Phonocardiography Bio-Medical Signals for Internet of Healthcare Things

“…One important issue when compressing the FPCG signal is that no signal degradation occurs; perhaps the matter's importance is because the signal's form carries vital information in diagnosing any problems in the heart. Generally, [16,17,18,19] and Block-Based Haar Wavelet Transform and COVIDOA [20] are commonly used signal compression techniques.…”

Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT)

“…In [14], the authors suggested a compression technique for bio-signals well-suited to IoMT use. This approach uses block-based HWT to isolate features from the input signal and then uses the innovative COVIDOA to choose the most significant characteristics for reconstruction.…”