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Article

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

1
IT Research Institute, Chosun University, Gwangju 61452, Korea
2
Department of Electrical and Computer Engineering, Alberta University, Edmonton, AB T6G 2R3, Canada
3
Department of Computer Science Engineering, National Institute of Technology Patna, Patna 800005, India
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7130; https://doi.org/10.3390/s20247130
Received: 6 November 2020 / Revised: 8 December 2020 / Accepted: 10 December 2020 / Published: 12 December 2020
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%. View Full-Text
Keywords: biometrics; user identification; post-exercise ECG; normalization; P wave; T wave; linear interpolation biometrics; user identification; post-exercise ECG; normalization; P wave; T wave; linear interpolation
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MDPI and ACS Style

Choi, G.H.; Ko, H.; Pedrycz, W.; Singh, A.K.; Pan, S.B. Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics. Sensors 2020, 20, 7130. https://doi.org/10.3390/s20247130

AMA Style

Choi GH, Ko H, Pedrycz W, Singh AK, Pan SB. Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics. Sensors. 2020; 20(24):7130. https://doi.org/10.3390/s20247130

Chicago/Turabian Style

Choi, Gyu H., Hoon Ko, Witold Pedrycz, Amit K. Singh, and Sung B. Pan 2020. "Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics" Sensors 20, no. 24: 7130. https://doi.org/10.3390/s20247130

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