Personal identity verification based ECG biometric using non-fiducial features

Elshahed, Marwa A.

doi:10.11591/ijece.v10i3.pp3007-3013

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…The ECG signal must be segmented for application to the user identification system and classification algorithm. Elshahed et al [22] proposed a user identification system that applied the discrete wavelet composition and Euclidean distance techniques by dividing ECG signals based on nonfiducial points. The method was validated on the ECG-ID and MIT-BIH arrhythmia databases.…”

Section: B Ecg Signal-based User Identification Systemmentioning

confidence: 99%

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

Kim,

Choi,

Choi

2023

IEEE Access

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Recent user recognition technologies have focused on biometric signals to remotely identify users in the access management, medical welfare, and public sectors. The electrocardiogram (ECG) signal is an individual's unique electrophysiological signal generated within the body and is difficult to forge or change; thus, it can be used to uniquely identify the user. Existing user identification systems based on ECG signals detect R peaks according to morphological features and use a fiducial-based segmentation process for data normalization. However, this process does not detect a distinct R peak due to motion artifacts generated by the movement of the subject, thereby degrading identification accuracy. To address the problem of decreasing peak accuracy of the fiducial-based segmentation data, this study proposes a user identification system based on one-dimensional neural networks using periodic non-fiducial-based segmentation data that do not overlap in the time domain. The proposed system comprises a preprocessing step for data denoising, a non-fiducial-based and non-overlap segmentation step, and a step for classifying users using a one-dimensional shallow neural network. In the experiment, when using self-acquired 10-second ECG signal data that were divided into non-fiducial and non-overlapping segments, the long short-term memory (LSTM), bidirectional LSTM, and one-dimensional convolutional neural network (CNN) models achieved accuracies of 92.01%, 93.13%, and 95.51%, respectively, with the 1D CNN model exhibiting the best accuracy. The proposed system demonstrated a 25.48% improvement in accuracy on non-fiducial segmented data compared to the 1D CNN model, which achieved an accuracy of 70.03% on fiducial segmented data. Moreover, when tested on publicly available ECG datasets, including MIT-BIH, ECG-ID, and PTB-XL, the proposed system exhibited a user identification accuracy of over 94%, confirming its superior performance.

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Section: B Ecg Signal-based User Identification Systemmentioning

confidence: 99%

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

Kim,

Choi,

Choi

2023

IEEE Access

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“…However, the frequency content of a baseline wander can be increased due to increased body movement during stress tests or exercise. The high pass filter is designed using Equation (1).…”

Section: Preprocessingmentioning

confidence: 99%

“…Biometrics provides unique features that can be utilized for authentication purposes. It includes Iris, Fingerprint, gait, etc, The electrocardiogram (ECG) (as shown in Figure 1) is one of the biometric traits that measure the dynamic electrical activity of the human heart [1]. It is considered an efficient method that can be utilized for identity recognition.…”

Section: Introductionmentioning

confidence: 99%

Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

et al. 2022

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The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER.

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Personal identity verification based ECG biometric using non-fiducial features

Cited by 2 publications

References 16 publications

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

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