Individual Identification Using ECG SignalsW

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

doi:10.4236/jcc.2018.61008

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The matching score is defined between 0% and 100% (with 100% being impossible to be realized). (c) Identification/recognition: It involves determining the identity of an unknown subject from a database of individuals [120]. Notably, the biometric system can then either attribute the identity corresponding to the most matching characterization found in the database…”

Section: ) General Scheme Of Ecg-based Biometric Systemmentioning

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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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: ) General Scheme Of Ecg-based Biometric Systemmentioning

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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“…Accordingly, the accuracy in Tantawi et al (2012) is indistinct while there is no testing for the unauthorized persons Yarong & Gang (2015), Gawande & Ladhake (2015) and Bassiouni et al (2016). Diab et al (2018) show how many subjects are used in testing authorized and unauthorized subjects.…”

Section: Figure 1 Enrolment Verification and Identification Processesmentioning

confidence: 99%

“…The accuracy of the identification system is 100%. Diab et al (2018) suggested an identification system based on ECG signal. This work forms a comparative study between different identification techniques and their performances.…”

Section: Related Workmentioning

confidence: 99%

Human Identification Using Electrocardiogram Signal as a Biometric Trait

Ibrahim

Abdel-Mageid

Nada

et al. 2021

International Journal of System Dynamics Applications

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Biometrics is an interesting study due to the incredible progress in security. Electrocardiogram (ECG) signal analysis is an active research area for diagnoses. Various techniques have been proposed in human identification system based on ECG. This work investigates in ECG as a biometric trait which based on uniqueness represented by physiological and geometrical of ECG signal of person.In this paper, a proposed non-fiducial identification system is presented with comparative study using Radial Basis Functions (RBF) neural network, Back Propagation (BP) neural network and Support Vector Machine (SVM) as classification methods. The Discrete Wavelet Transform method is applied to extract features from the ECG signal. The experimental results show that the proposed scheme achieves high identification rate compared to the existing techniques. Furthermore, the two classifiers RBF and BP are integrated to achieve higher rate of human identification.

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“…Non-fiducial features are obtained by using several methods from the transformed domain. These features needless computation time than the fiducial features also it doesn't require to find boundaries of the ECG waveforms which change constantly [7][8][9]. Two ECG approaches are available based on the used features which are fiducial and non-fiducial approaches as shown in Figure 3.…”

Section: Introductionmentioning

confidence: 99%

Personal identity verification based ECG biometric using non-fiducial features

Elshahed¹

2020

IJECE

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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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Individual Identification Using ECG SignalsW

Cited by 7 publications

References 7 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

Human Identification Using Electrocardiogram Signal as a Biometric Trait

Personal identity verification based ECG biometric using non-fiducial features

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