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Journal articles on the topic 'ECG biometry'

Author: Grafiati

Published: 28 June 2021

Last updated: 14 February 2022

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1

Luz, Eduardo José da S., David Menotti, and William Robson Schwartz. "Evaluating the use of ECG signal in low frequencies as a biometry." Expert Systems with Applications 41, no. 5 (April 2014): 2309–15. http://dx.doi.org/10.1016/j.eswa.2013.09.028.

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2

Kumar, Sunil. "ECG Biometric Identification." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 2148–52. http://dx.doi.org/10.22214/ijraset.2018.3505.

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3

Yoon, Seok-Joo, and Gwang-Jun Kim. "Personal Biometric Identification based on ECG Features." Journal of the Korea institute of electronic communication sciences 10, no. 4 (April 30, 2015): 521–26. http://dx.doi.org/10.13067/jkiecs.2015.10.4.521.

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4

Karimian, Nima, Damon Woodard, and Domenic Forte. "ECG Biometric: Spoofing and Countermeasures." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (July 2020): 257–70. http://dx.doi.org/10.1109/tbiom.2020.2992274.

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5

Donida Labati, Ruggero, Enrique Muñoz, Vincenzo Piuri, Roberto Sassi, and Fabio Scotti. "Deep-ECG: Convolutional Neural Networks for ECG biometric recognition." Pattern Recognition Letters 126 (September 2019): 78–85. http://dx.doi.org/10.1016/j.patrec.2018.03.028.

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6

Yadav, Nisha, Manoj Duhan, and Anil Rose. "Biometric Human Recognition using ECG Signals." IARJSET 4, no. 6 (June 20, 2017): 168–71. http://dx.doi.org/10.17148/iarjset.2017.4630.

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7

Odinaka, Ikenna, Po-Hsiang Lai, Alan D. Kaplan, Joseph A. O'Sullivan, Erik J. Sirevaag, and John W. Rohrbaugh. "ECG Biometric Recognition: A Comparative Analysis." IEEE Transactions on Information Forensics and Security 7, no. 6 (December 2012): 1812–24. http://dx.doi.org/10.1109/tifs.2012.2215324.

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8

Ingale, Mohit, Renato Cordeiro, Siddartha Thentu, Younghee Park, and Nima Karimian. "ECG Biometric Authentication: A Comparative Analysis." IEEE Access 8 (2020): 117853–66. http://dx.doi.org/10.1109/access.2020.3004464.

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9

Kadhim Abed, Jameel, and Nahrain N. Abd. "ECG Biometric Verification by using PCA." International Journal of Engineering Trends and Technology 65, no. 1 (November 25, 2018): 4–8. http://dx.doi.org/10.14445/22315381/ijett-v65p202.

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10

BE, Manjunathswamy, Appaji M Abhishek, Thriveni J, Venugopal K R, and L. M Patnaik. "Multimodal Biometric Authentication using ECG and Fingerprint." International Journal of Computer Applications 111, no. 13 (February 18, 2015): 33–39. http://dx.doi.org/10.5120/19601-1452.

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WU, Hung-Tsai, Yi-Ting WU, and Wen-Whei CHANG. "Biometric Identification Using JPEG2000 Compressed ECG Signals." IEICE Transactions on Information and Systems E98.D, no. 10 (2015): 1829–37. http://dx.doi.org/10.1587/transinf.2015edp7035.

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12

Astapov, A. A., D. V. Davydov, A. I. Egorov, D. V. Drozdov, and E. M. Glukhovskij. "ECG-based biometric identification: some modern approaches." Bulletin of Russian State Medical University, no. 1 (2016): 35–39. http://dx.doi.org/10.24075/brsmu.2016-01-06.

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13

Hammad, Mohamed, Gongning Luo, and Kuanquan Wang. "Cancelable biometric authentication system based on ECG." Multimedia Tools and Applications 78, no. 2 (July 3, 2018): 1857–87. http://dx.doi.org/10.1007/s11042-018-6300-2.

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14

Rezgui, Dhouha, and Zied Lachiri. "ECG biometric recognition using SVM-based approach." IEEJ Transactions on Electrical and Electronic Engineering 11 (June 2016): S94—S100. http://dx.doi.org/10.1002/tee.22241.

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15

Wu, Shun-Chi, Pei-Lun Hung, and A. Lee Swindlehurst. "ECG Biometric Recognition: Unlinkability, Irreversibility, and Security." IEEE Internet of Things Journal 8, no. 1 (January 1, 2021): 487–500. http://dx.doi.org/10.1109/jiot.2020.3004362.

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16

El_Rahman, Sahar A. "Biometric human recognition system based on ECG." Multimedia Tools and Applications 78, no. 13 (January 8, 2019): 17555–72. http://dx.doi.org/10.1007/s11042-019-7152-0.

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17

Agrafioti, Foteini, and Dimitrios Hatzinakos. "ECG biometric analysis in cardiac irregularity conditions." Signal, Image and Video Processing 3, no. 4 (September 2, 2008): 329–43. http://dx.doi.org/10.1007/s11760-008-0073-4.

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18

Cutolo, Carlo Alberto, Chiara Bonzano, Carlo Catti, Alessandro Bagnis, Riccardo Scotto, Letizia Negri, Sara Olivari, Francesca Cappelli, and Carlo Enrico Traverso. "Predictors of Endothelial Cell Loss after Phacoemulsification for the Treatment of Primary Angle Closure." Journal of Ophthalmology 2019 (July 28, 2019): 1–5. http://dx.doi.org/10.1155/2019/6368784.

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Purpose. To investigate demographic and anatomical factors associated with a reduction in endothelial cell density (ECD) after phacoemulsification (PE) for the treatment of primary angle closure (PAC). Methods. In this prospective case series, ECD was evaluated by noncontact specular microscopy and biometric parameters by both noncontact optical biometry and anterior segment optical coherence tomography, preoperatively and at 12 months after surgery. Anterior segment biomicroscopy and gonioscopy were also performed. The change in ECD and its relation to clinical characteristics and biometric parameters were evaluated by linear regression analysis. Results. 44 patients with PAC were included in the study. The mean (SD) patient age was 71.6 (10.2) years; thirty-one (70.5%) of them were women. Coexistence of exfoliation syndrome (XS) was observed in 4 cases (9.1%). The mean (SD) ECD (cells/mm2) changed from 2275 (463) preoperatively to 1964 (613) postoperatively with a mean reduction of −310 (95% CI −445 to −176; p<0.001). In the multivariate regression model, after correction for age and lens status, XS was the only parameter associated with ECD percentage change (B = −36.00; p=0.001). Conclusion. PE in angle closure causes a significant ECD reduction. In our population of PAC patients, XS is significantly associated with ECD change. In this group of patients, a careful preoperative endothelial evaluation should be performed.

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19

Patro, Kiran Kumar, and P. Rajesh Kumar. "Effective Feature Extraction of ECG for Biometric Application." Procedia Computer Science 115 (2017): 296–306. http://dx.doi.org/10.1016/j.procs.2017.09.138.

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20

Dudykevych, V. B., V. V. Khoma, V. F. Chekurin, Y. V. Khoma, and D. V. Sabodashko. "ECG SIGNALS NORMALIZATION FOR SYSTEMS OF BIOMETRIC IDENTIFICATION." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 4, no. 1 (2019): 49–56. http://dx.doi.org/10.32838/2663-5941/2019.4-1/10.

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21

Bugdol, Marcin D., and Andrzej W. Mitas. "Multimodal biometric system combining ECG and sound signals." Pattern Recognition Letters 38 (March 2014): 107–12. http://dx.doi.org/10.1016/j.patrec.2013.11.014.

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22

Sidek, Khairul Azami, Vu Mai, and Ibrahim Khalil. "Data mining in mobile ECG based biometric identification." Journal of Network and Computer Applications 44 (September 2014): 83–91. http://dx.doi.org/10.1016/j.jnca.2014.04.008.

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23

Porée, Fabienne, Gaëlle Kervio, and Guy Carrault. "ECG biometric analysis in different physiological recording conditions." Signal, Image and Video Processing 10, no. 2 (December 30, 2014): 267–76. http://dx.doi.org/10.1007/s11760-014-0737-1.

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24

Merone, Mario, Paolo Soda, Mario Sansone, and Carlo Sansone. "ECG databases for biometric systems: A systematic review." Expert Systems with Applications 67 (January 2017): 189–202. http://dx.doi.org/10.1016/j.eswa.2016.09.030.

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25

Sufi, Fahim, Ibrahim Khalil, and Ibrahim Habib. "Polynomial distance measurement for ECG based biometric authentication." Security and Communication Networks 3, no. 4 (July 2010): 303–19. http://dx.doi.org/10.1002/sec.76.

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26

Zhou, Ruishi, Chenshuo Wang, Pengfei Zhang, Xianxiang Chen, Lidong Du, Peng Wang, Zhan Zhao, Mingyan Du, and Zhen Fang. "ECG-based biometric under different psychological stress states." Computer Methods and Programs in Biomedicine 202 (April 2021): 106005. http://dx.doi.org/10.1016/j.cmpb.2021.106005.

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27

Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti, and Khan Muhammad. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (March 10, 2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

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28

Behrensen, Maren. "Identity as convention: biometric passports and the promise of security." Journal of Information, Communication and Ethics in Society 12, no. 1 (March 4, 2014): 44–59. http://dx.doi.org/10.1108/jices-08-2013-0029.

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Purpose – The paper is a conceptual investigation of the metaphysics of personal identity and the ethics of biometric passports. The paper aims to discuss these issues. Design/methodology/approach – Philosophical argument, discussing both the metaphysical and the social ethics/computer ethics literature on personal identity and biometry. Findings – The author argues for three central claims in this paper: passport are not simply representations of personal identity, they help constitute personal identity. Personal identity is not a metaphysical fact, but a set of practices, among them identity management practices (e.g. population registries) employed by governments. The use of biometry as part of these identity management practices is not an ethical problem as such, nor is it something fundamentally new and different compared to older ways of establishing personal identity. It is worrisome, however, since in the current political climate, it is systematically used to deny persons access to specific territories, rights, and benefits. Originality/value – The paper ties together strands of philosophical inquiry that do not usually converse with one another, namely the metaphysics of personal identity, and the topic of identity in social philosophy and computer ethics.

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29

Alhayajneh, Abdullah, Alessandro Baccarini, Gary Weiss, Thaier Hayajneh, and Aydin Farajidavar. "Biometric Authentication and Verification for Medical Cyber Physical Systems." Electronics 7, no. 12 (December 14, 2018): 436. http://dx.doi.org/10.3390/electronics7120436.

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A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g., ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

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30

AlDuwaile, Dalal A., and Md Saiful Islam. "Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition." Entropy 23, no. 6 (June 9, 2021): 733. http://dx.doi.org/10.3390/e23060733.

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The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

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31

Shdefat, Ahmed Younes, Moon-Il Joo, Sung-Hoon Choi, and Hee-Cheol Kim. "Utilizing ECG Waveform Features as New Biometric Authentication Method." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 658. http://dx.doi.org/10.11591/ijece.v8i2.pp658-665.

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<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>

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32

Safie, Sairul I., John J. Soraghan, and Lykourgos Petropoulakis. "Electrocardiogram (ECG) Biometric Authentication Using Pulse Active Ratio (PAR)." IEEE Transactions on Information Forensics and Security 6, no. 4 (December 2011): 1315–22. http://dx.doi.org/10.1109/tifs.2011.2162408.

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33

Lourenço, André, Hugo Silva, and Ana Fred. "Unveiling the Biometric Potential of Finger-Based ECG Signals." Computational Intelligence and Neuroscience 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/720971.

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The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

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34

Cui, Wei, Zihan Wang, and Yaoguang Li. "ECG-based biometric recognition under exercise and rest situations." Biomedical Engineering Advances 2 (December 2021): 100008. http://dx.doi.org/10.1016/j.bea.2021.100008.

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35

Elshahed, Marwa A. "Personal identity verification based ECG biometric using non-fiducial features." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3007. http://dx.doi.org/10.11591/ijece.v10i3.pp3007-3013.

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Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

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36

Ramos, Mariana S., João M. Carvalho, Armando J. Pinho, and Susana Brás. "On the Impact of the Data Acquisition Protocol on ECG Biometric Identification." Sensors 21, no. 14 (July 7, 2021): 4645. http://dx.doi.org/10.3390/s21144645.

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Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

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37

Wang, Di, Yujuan Si, Weiyi Yang, Gong Zhang, and Tong Liu. "A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification." Applied Sciences 9, no. 1 (January 8, 2019): 201. http://dx.doi.org/10.3390/app9010201.

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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).

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38

Barros, Alex, Paulo Resque, João Almeida, Renato Mota, Helder Oliveira, Denis Rosário, and Eduardo Cerqueira. "Data Improvement Model Based on ECG Biometric for User Authentication and Identification." Sensors 20, no. 10 (May 21, 2020): 2920. http://dx.doi.org/10.3390/s20102920.

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The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

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39

Belgacem, Noureddine, Régis Fournier, Amine Nait-Ali, and Fethi Bereksi-Reguig. "A novel biometric authentication approach using ECG and EMG signals." Journal of Medical Engineering & Technology 39, no. 4 (April 2, 2015): 226–38. http://dx.doi.org/10.3109/03091902.2015.1021429.

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40

Chou, Ching-Yao, Yo-Woei Pua, Ting-Wei Sun, and An-Yeu (Andy) Wu. "Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis." Sensors 20, no. 11 (June 9, 2020): 3279. http://dx.doi.org/10.3390/s20113279.

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Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

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41

Wahabi, Saeid, Shahrzad Pouryayevali, Siddarth Hari, and Dimitrios Hatzinakos. "On Evaluating ECG Biometric Systems: Session-Dependence and Body Posture." IEEE Transactions on Information Forensics and Security 9, no. 11 (November 2014): 2002–13. http://dx.doi.org/10.1109/tifs.2014.2360430.

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42

Li, Yazhao, Yanwei Pang, Kongqiao Wang, and Xuelong Li. "Toward improving ECG biometric identification using cascaded convolutional neural networks." Neurocomputing 391 (May 2020): 83–95. http://dx.doi.org/10.1016/j.neucom.2020.01.019.

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43

El-Refaey, Amir, Marwa Shouman, Ezz El-din Hemdan, Adel EL-Fishawy, and Fathi Abd El-Samie. "Triple C: A New Algorithm for ECG Cancelable Biometric System." Menoufia Journal of Electronic Engineering Research 28, no. 1 (December 7, 2019): 43–50. http://dx.doi.org/10.21608/mjeer.2019.67376.

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44

Sidek, Khairul A., Ibrahim Khalil, and Herbert F. Jelinek. "ECG Biometric with Abnormal Cardiac Conditions in Remote Monitoring System." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 11 (November 2014): 1498–509. http://dx.doi.org/10.1109/tsmc.2014.2336842.

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45

Benouis, Mohamed, Lotfi Mostefai, Nicholas Costen, and Meryem Regouid. "ECG based biometric identification using one-dimensional local difference pattern." Biomedical Signal Processing and Control 64 (February 2021): 102226. http://dx.doi.org/10.1016/j.bspc.2020.102226.

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Tantawi, M. M., K. Revett, A. Salem, and M. F. Tolba. "Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition." Journal of Intelligent Information Systems 40, no. 1 (July 20, 2012): 17–39. http://dx.doi.org/10.1007/s10844-012-0214-7.

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Kim, Junmo, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko Keun Kim, and Cheolsoo Park. "Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning." Sensors 21, no. 5 (February 24, 2021): 1568. http://dx.doi.org/10.3390/s21051568.

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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Zheng, Gang, Ya Rong Wang, Qin Qin, Yang Li, and Zhong Yi Li. "Comparative Study of ECG Based Identification." Applied Mechanics and Materials 713-715 (January 2015): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.700.

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ECG waveform has becoming a new kind of Biometric signal for person identification recently. The identification procedure was based on the establishment of person's own ECG waveform template, one beat of ECG waveform was used as analysis data, 15 persons were involved in the study. In the paper, several algorithms are compared for their ability to identify person. The algorithms are coefficient threshold method and Euclidean distance threshold method, their measurement was based on linear distance. Hausdorff distance threshold method as a non linear distance was also compared. Except this, SWM(support vector machine) was also introduced to do the identification work. From the experimental results, Euclidean distance threshold method and the Hausdorff distance threshold method reached same level of identification, the acceptance rate is around 76.0%, the correlation coefficient threshold method reached 86.0%. As to the SVM, its acceptance rate near 96.0%. Although the amount of experiment data was relatively small, but the result give the researching in good promising and better prospecting.

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Tirado-Martin, Paloma, and Raul Sanchez-Reillo. "BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets." Applied Sciences 11, no. 13 (June 24, 2021): 5880. http://dx.doi.org/10.3390/app11135880.

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Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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Tseng, Kuo-Kun, Jiao Luo, Robert Hegarty, Wenmin Wang, and Dong Haiting. "Sparse Matrix for ECG Identification with Two-Lead Features." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/656807.

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Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

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