A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
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...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | http://www.mdpi.com/2076-3417/9/1/201 |
id |
doaj-1e96a660c85a427cbf46692c36b3bdac |
---|---|
record_format |
Article |
spelling |
doaj-1e96a660c85a427cbf46692c36b3bdac2020-11-25T02:12:24ZengMDPI AGApplied Sciences2076-34172019-01-019120110.3390/app9010201app9010201A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric IdentificationDi Wang0Yujuan Si1Weiyi Yang2Gong Zhang3Tong Liu4College of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaSchool of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaIn 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).http://www.mdpi.com/2076-3417/9/1/201ECG identificationshort-term ECG signalsHR-free resampling strategyprincipal component analysis networkECG-ID |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Di Wang Yujuan Si Weiyi Yang Gong Zhang Tong Liu |
spellingShingle |
Di Wang Yujuan Si Weiyi Yang Gong Zhang Tong Liu A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification Applied Sciences ECG identification short-term ECG signals HR-free resampling strategy principal component analysis network ECG-ID |
author_facet |
Di Wang Yujuan Si Weiyi Yang Gong Zhang Tong Liu |
author_sort |
Di Wang |
title |
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification |
title_short |
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification |
title_full |
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification |
title_fullStr |
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification |
title_full_unstemmed |
A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification |
title_sort |
novel heart rate robust method for short-term electrocardiogram biometric identification |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-01-01 |
description |
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). |
topic |
ECG identification short-term ECG signals HR-free resampling strategy principal component analysis network ECG-ID |
url |
http://www.mdpi.com/2076-3417/9/1/201 |
work_keys_str_mv |
AT diwang anovelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT yujuansi anovelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT weiyiyang anovelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT gongzhang anovelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT tongliu anovelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT diwang novelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT yujuansi novelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT weiyiyang novelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT gongzhang novelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification AT tongliu novelheartraterobustmethodforshorttermelectrocardiogrambiometricidentification |
_version_ |
1724909621945040896 |