ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Hanilçi, Ayça; Gürkan, Hakan

doi:10.38088/jise.559236

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…The system was evaluated on different ECG databases. Similarly, [41] proposed a two-dimensional convolutional neural network (2D CNN). Firstly, ACDCT features and cepstral properties were extracted from ECG signals.…”

Section: Ecg-based Recognition Methodsmentioning

confidence: 99%

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Aleidan,

Abbas,

Daadaa

et al. 2023

Applied Sciences

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User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.

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Section: Ecg-based Recognition Methodsmentioning

confidence: 99%

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Aleidan,

Abbas,

Daadaa

et al. 2023

Applied Sciences

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“…The user identification performance using the 1D CNN model was found to be the highest for all overlap data, and it was confirmed that the performance improved as the amount of data increased. Hanil et al [34] proposed a two-dimensional electrocardiogram biometric method that extracts attributes from overlapped ECG signals. Using the PTB-XL database, the identification accuracy was validated at 85.71% for the ACDCT image and at 80.95% for the CC image after fiducial-based segmentation.…”

Section: User Identification Accuracy Analysis By Data Overlapmentioning

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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“…The recognition rate was 100% for the first and the second datasets, while it is 98.33% for the third dataset. Hanilçi and Gürkan, (2019) presented an ECG biometric identification method based on a two-dimensional convolutional neural network. AC/DCT features, and cepstral features were extracted from ECG signals.…”

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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ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Cited by 9 publications

References 23 publications

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

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

Human Identification Using Electrocardiogram Signal as a Biometric Trait

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