ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions
The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/6966 |
| _version_ | 1797511863116759040 |
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| author | Ho Bin Hwang Hyeokchan Kwon Byungho Chung Jongshill Lee In Young Kim |
| author_facet | Ho Bin Hwang Hyeokchan Kwon Byungho Chung Jongshill Lee In Young Kim |
| author_sort | Ho Bin Hwang |
| collection | DOAJ |
| description | The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions. |
| first_indexed | 2024-03-10T05:53:01Z |
| format | Article |
| id | doaj.art-840925f3ee994d7c92638ddd9bae704c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:01Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-840925f3ee994d7c92638ddd9bae704c2023-11-22T21:34:35ZengMDPI AGSensors1424-82202021-10-012121696610.3390/s21216966ECG Authentication Based on Non-Linear Normalization under Various Physiological ConditionsHo Bin Hwang0Hyeokchan Kwon1Byungho Chung2Jongshill Lee3In Young Kim4Department of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaInformation Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaInformation Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaThe development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.https://www.mdpi.com/1424-8220/21/21/6966biometricsauthenticationvarious physiological conditionsECGnon-linearnormalization |
| spellingShingle | Ho Bin Hwang Hyeokchan Kwon Byungho Chung Jongshill Lee In Young Kim ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions Sensors biometrics authentication various physiological conditions ECG non-linear normalization |
| title | ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions |
| title_full | ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions |
| title_fullStr | ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions |
| title_full_unstemmed | ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions |
| title_short | ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions |
| title_sort | ecg authentication based on non linear normalization under various physiological conditions |
| topic | biometrics authentication various physiological conditions ECG non-linear normalization |
| url | https://www.mdpi.com/1424-8220/21/21/6966 |
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