A proposed authentication model using Siamese networks and ECG signals achieved 92% and 95% accuracy on the ECD-ID and PTB datasets, respectively.
Does a Siamese network using ECG signals provide high authentication accuracy for digital healthcare systems?
A Siamese network using ECG signals can provide highly accurate authentication for digital healthcare systems, overcoming some limitations of traditional machine learning models.
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.
Behrouzi et al. (Tue,) conducted a other in Authentication in digital healthcare systems. Siamese networks using ECG signals was evaluated on Accuracy. A proposed authentication model using Siamese networks and ECG signals achieved 92% and 95% accuracy on the ECD-ID and PTB datasets, respectively.
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