Development of Optimized User-Recognition Technology Using Multilayered XAI-Based ECG Signals

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.