A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges
Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-fr...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9475452/ |
| _version_ | 1818618567817428992 |
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| author | Anthony Ngozichukwuka Uwaechia Dzati Athiar Ramli |
| author_facet | Anthony Ngozichukwuka Uwaechia Dzati Athiar Ramli |
| author_sort | Anthony Ngozichukwuka Uwaechia |
| collection | DOAJ |
| description | Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-frequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems. |
| first_indexed | 2024-12-16T17:23:39Z |
| format | Article |
| id | doaj.art-9d4dd5a3f34e4fe489519c9f155ae559 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:23:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9d4dd5a3f34e4fe489519c9f155ae5592022-12-21T22:23:07ZengIEEEIEEE Access2169-35362021-01-019977609780210.1109/ACCESS.2021.30952489475452A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future ChallengesAnthony Ngozichukwuka Uwaechia0https://orcid.org/0000-0002-1797-6558Dzati Athiar Ramli1https://orcid.org/0000-0002-4392-2895Department of Electrical and Computer Engineering, Baze University, Abuja, NigeriaSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, MalaysiaElectrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-frequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.https://ieeexplore.ieee.org/document/9475452/ECG biometricsapplications of biometricbiometric traitsfeature extractionfeature learningclassification |
| spellingShingle | Anthony Ngozichukwuka Uwaechia Dzati Athiar Ramli A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges IEEE Access ECG biometrics applications of biometric biometric traits feature extraction feature learning classification |
| title | A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges |
| title_full | A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges |
| title_fullStr | A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges |
| title_full_unstemmed | A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges |
| title_short | A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges |
| title_sort | comprehensive survey on ecg signals as new biometric modality for human authentication recent advances and future challenges |
| topic | ECG biometrics applications of biometric biometric traits feature extraction feature learning classification |
| url | https://ieeexplore.ieee.org/document/9475452/ |
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