The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

Li, Ning, He, Fuxing, Ma, Wentao, Wang, Ruotong, Jiang, Lin ORCID: 0000-0001-6531-2791 and Zhang, Xiaoping (2022) The Identification of ECG Signals Using Wavelet Transform and WOA-PNN SENSORS, 22 (12). 4343-. ISSN 1424-8220, 1424-8220

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Abstract

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.

Item Type:	Article
Uncontrolled Keywords:	electrocardiogram signal identification, mean impact value, probabilistic neural network, wavelet transform, whale optimization algorithm
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	20 Jul 2022 09:21
Last Modified:	23 May 2026 06:47
DOI:	10.3390/s22124343
Open Access URL:	https://doi.org/10.3390/s22124343
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3158946
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