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Open AccessArticle

A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding

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
College of Instrument Science and Electrical Engineering, Jilin University, Changchun 130061, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(6), 667; https://doi.org/10.3390/electronics8060667
Received: 25 April 2019 / Revised: 5 June 2019 / Accepted: 10 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the influence of noise interference and realize accurate identification, a novel temporal-frequency autoencoding based method is proposed. In particular, the raw data is firstly transformed into the wavelet domain, where multi-level time-frequency representation is achieved. Then, a prior knowledge-based feature selection is proposed and applied to the transformed data to discard noise components and retain identity-related information simultaneously. Afterward, the stacked sparse autoencoder is introduced to learn intrinsic discriminative features from the selected data, and Softmax classifier is used to perform the identification task. The effectiveness of the proposed method is evaluated on two public databases, namely, ECG-ID and Massachusetts Institute of Technology-Biotechnology arrhythmia (MIT-BIH-AHA) databases. Experimental results show that our method can achieve high multiple-heartbeat identification accuracies of 98.87%, 92.3%, and 96.82% on raw ECG signals which are from the ECG-ID (Two-recording), ECG-ID (All-recording), and MIT-BIH-AHA database, respectively, indicating that our method can provide an efficient way for ECG biometric identification. View Full-Text
Keywords: ECG identification; multi-level time-frequency representation; prior knowledge-based feature selection; stacked sparse autoencoder ECG identification; multi-level time-frequency representation; prior knowledge-based feature selection; stacked sparse autoencoder
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Wang, D.; Si, Y.; Yang, W.; Zhang, G.; Li, J. A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding. Electronics 2019, 8, 667.

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