BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

Tirado-Martin, Paloma; Sánchez-Reillo, Raúl

doi:10.3390/app11135880

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…Earlier research papers have reported that the CNN, LSTM, and CNN-LSTM architectures for ECG authentication were able to achieve a classification accuracy of over 90% [ 35 ]. Although none of the proposed models offer a direct authentication approach that can be implemented without post-processing after classification, supervised DNN-based models, like classifiers, are commonly employed for authentication [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

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Section: Related Workmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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show abstract

“…Using a BLSTM network, the suggested data-independent approach in [ 36 ] achieves a relatively better identification accuracy for each database. The proposed technique in [ 37 ] was tested using deep learning methods such as CNN and LSTM on self-collected databases S1 and S2, with the experimental results showing an EER% of 0–10.13% for the S2 database and the mixed S2 and S1 datasets, 0–5.31% for one-day enrollment, and 0.033–3.93% and 0–1.35% for two-day enrollment. The study in [ 38 ] proposed using an electrocardiogram and an ensemble of CNN and LSTM for personal identification, using the CU-ECG database created by Chosun University, which showed an accuracy greater than 90% in identifying individuals based on ECG signal characteristics.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Asif

Faisal

Dar

et al. 2023

Sensors

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The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

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“…The above structures CNN, LSTM, and CNN-LSTM for ECG authentication in previously written papers provided as classifi cation [22]. They reached more than 90% accuracy in classifi cation.…”

Section: Related Workmentioning

confidence: 99%

Using ECG Signals in Siamese Networks for Authentication in Digital Healthcare Systems

Behrouzi¹,

Shirkani²,

Fard³

2022

J Biomed Res Environ Sci

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In digital healthcare systems, with digitalization, data can be easily accessed. Considering the sensitivity of confidential information, the need for security is accelerated during this time. One of the most important security aspects is authentication which should be utilized. The available authentication models that rely on Machine Learning (ML) have some shortcomings, such as difficulties in appending new users to the system or model training sensitivity to imbalanced data. To address these problems, we propose an application of the Siamese networks using ECG signals which are easily reachable in digital healthcare systems. Adding some preprocessing for feature extraction in such a model could lead us to prominent results. This model is performed on ECD-ID and PTB datasets and approaches 92% and 95% of accuracy, respectively. A combination of simplicity and high performance made it an exclusive choice for smart healthcare and telehealth.

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BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

Cited by 11 publications

References 33 publications

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Using ECG Signals in Siamese Networks for Authentication in Digital Healthcare Systems

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