Key result
A multilayered XAI-based deep learning model for ECG user recognition achieved higher accuracy than a single model, with minimum accuracy differences of 15% and 0.2%.
Why the study?
Deep learning using ECG signals is being developed as a next-generation user-recognition technology, prompting the proposal of a model combining multilayered explainable artificial intelligence for real-life application.
A multilayered XAI-based deep learning model using ECG signals provides higher and more stable accuracy for personal identification compared to a single deep learning model.
Supports multilayered DL for stable ECG biometrics; leaves open prospective validation before clinical use.
Owing to the swift advancements in information and technology and wearable device technology, research involving bio-signals is being conducted for convenient personal identification authentication. Therefore, deep learning using electrocardiogram (ECG) signals is being developed as a next-generation user-recognition technology for application in real-life environments based on high security and accuracy. This study proposes a deep learning model that combines multilayered explainable artificial intelligence for user recognition in a real-life environment. The ECG-signal database utilizes the MIT-BIH normal sinus rhythm database (NSRDB) and a self-acquired database. A comparison between a single deep learning model and the proposed multilayer deep learning model using these signals confirmed that the proposed model achieved higher accuracy, with minimum accuracy differences of 15% and 0.2%. In addition, the recognition accuracies of the single deep learning model and multilayered deep learning model were compared five times to confirm the difference in output results caused by various factors in the single deep learning model. The user-recognition accuracies of a single deep learning model for the NSRDB exhibited a difference of 3.4%, whereas those of the multilayered model showed a difference of 0.9%. The user-recognition accuracy difference when applying the self-acquired database to the single deep learning model was 1.3%, but that of the multilayered deep learning model was 0.3%. Therefore, the multilayered deep learning model exhibited a stable user-recognition accuracy compared to that of the single structure, and the optimized area that affects the output result was confirmed.
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Jaehyo Jung (2023) studied Personal identification authentication. Multilayered XAI-based deep learning model vs. Single deep learning model was evaluated on User-recognition accuracy. A multilayered XAI-based deep learning model for ECG user recognition achieved higher accuracy than a single model, with minimum accuracy differences of 15% and 0.2%.
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