A Review on ECG-Based Biometric Authentication Systems

Diab, Mohamad O.; Seif, A.; Sabbah, Maher; El-Abed, Mohamad; Aloulou, Nijez

doi:10.1007/978-981-13-0956-4_2

Cited by 17 publications

(12 citation statements)

References 24 publications

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“…Topics regarding ECG classification and deep learning techniques are still missing. [80] 2020 Topics discussed include the existing ECG benchmarks, fiducial and nonfiducial features authentication and methods, and data mining classification techniques.…”

Section: Yearmentioning

confidence: 99%

“…However, topics regarding classifiers and deep learning techniques are still missing. In [80], a review of ECG-based authentication systems is presented and topics discussed include the existing ECG benchmarks, fiducial and non-fiducial features authentication and methods, and data mining classification techniques. However, topics regarding ECG feature transformation, classification, and deep learning techniques are still missing.…”

Section: Yearmentioning

confidence: 99%

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A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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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/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing 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 selected success measures and proper structure of the ECG signal database 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.

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Section: Yearmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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“…The individual’s behavioral attributes can involve handwritten signature [ 6 ], voice [ 7 ], gait dynamics [ 8 ], and others. Falsification of conventional biometrics data can be overcome by using bioelectric signals such as electrocardiographic (ECG) [ 9 ], electromyographic (EMG) [ 10 ], electrooculographic (EOG) [ 11 ], and electroencephalographic (EEG) [ 12 ] signals, for personal identification and verification. These signals contain unique patterns that are difficult to copy or imitate, therefore preserving the secrecy and privacy of the individuals.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Person Identification during Escalating Cognitive Load

Kralikova

Babušiak

Šmondrk

2022

Sensors

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With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

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“…The ECG is an electrical signal acquired through an electrode attached to the skin and consists of a P wave, QRS complex, and T wave [13]. There are three reasons why ECG patterns vary from person to person [14]. The first is a physiological factor that reflects heart size, mass, conductivity, and activity, all of which differ from person to person.…”

Section: Introductionmentioning

confidence: 99%

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

Hwang

Kwon

Chung

et al. 2021

Sensors

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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.

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A Review on ECG-Based Biometric Authentication Systems

Cited by 17 publications

References 24 publications

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

EEG-Based Person Identification during Escalating Cognitive Load

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

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