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Appl. Sci. 2019, 9(1), 201; https://doi.org/10.3390/app9010201

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

1
College of Communication Engineering, Jilin University, Changchun 130012, China
2
School of Electronic and Information Engineering (SEIE), Zhuhai College, Jilin University, Zhuhai 519041, China
3
School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
*
Author to whom correspondence should be addressed.
Received: 8 December 2018 / Revised: 29 December 2018 / Accepted: 3 January 2019 / Published: 8 January 2019
(This article belongs to the Special Issue Advanced Biometrics with Deep Learning)
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Abstract

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices). View Full-Text
Keywords: ECG identification; short-term ECG signals; HR-free resampling strategy; principal component analysis network; ECG-ID ECG identification; short-term ECG signals; HR-free resampling strategy; principal component analysis network; ECG-ID
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Wang, D.; Si, Y.; Yang, W.; Zhang, G.; Liu, T. A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification. Appl. Sci. 2019, 9, 201.

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