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

Behrouzi, Pouria; Shirkani, Bahareh; Fard, Seyed Mehdi Hazrati

doi:10.37871/jbres1605

Cited by 1 publication

(4 citation statements)

References 15 publications

(16 reference statements)

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“…Our model shows 93.6% average accuracy, 1.69% FAR, 1.84% FRR, and 1.76% EER, which outperforms the rivals. The rival models include the proposed models in [ 6 , 23 , 38 ] that used ECG signals in the Siamese network for authentication in their paper. Also, to find the best structure in the proposed model, we used CNN-LSTM, LSTM, and Bi-LSTM structures in a similar Siamese network using the same preprocessing as our proposed model.…”

Section: Resultsmentioning

confidence: 99%

“…Our model shows 96.8% average accuracy, 1.69% FAR, 1.73% FRR, and 1.66% EER on the PTB dataset. Again the rival models include the proposed models in [ 6 , 23 , 38 ] and also using CNN-LSTM, LSTM, and Bi-LSTM structures with the same preprocessing as our proposed model.…”

Section: Resultsmentioning

confidence: 99%

“…The Siamese network was utilized in our prior study [ 23 ] to tackle challenges related to limited sample availability and imbalanced scenarios in the context of ECG-based authentication. A major concern with all available methods is that they have been evaluated only on standard data collected on lab conditions.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…We used the Siamese network in our previous research paper [ 23 ] to perform authentication using ECG signals. The current study improves the ability to deal with ECG signals in diverse situations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Resultsmentioning

confidence: 99%