Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1230
• Main Authors: Baek, Suwhan, Kim, Juhyeong, Yu, Hyunsoo, Yang, Geunbo, Sohn, Illsoo, Cho, Youngho, Park, Cheolsoo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.01.2023; MDPI
• Subjects: Algorithms; authentication; Bayes Theorem; Biometrics; Classification; Data encryption chips; ECG; Electrocardiogram; Electrocardiography; Electrocardiography - methods; Experiments; Feature selection; Humans; hyperparameter optimization; Intelligence; Internet of Things; Neural networks; Optimization; Personal identification numbers; reinforcement learning; Research methodology; Wearable Electronic Devices; Germany; ECG; biometrics; hyperparameter optimization; feature selection; authentication; reinforcement learning
• DOI: 10.3390/s23031230
• ISSN: 1424-8220

In this study, the optimal features of electrocardiogram (ECG) signals were investigated for the implementation of a personal authentication system using a reinforcement learning (RL) algorithm. ECG signals were recorded from 11 subjects for 6 days. Consecutive 5-day datasets (from the 1st to the 5th day) were trained, and the 6th dataset was tested. To search for the optimal features of ECG for the authentication problem, RL was utilized as an optimizer, and its internal model was designed based on deep learning structures. In addition, the deep learning architecture in RL was automatically constructed based on an optimization approach called Bayesian optimization hyperband. The experimental results demonstrate that the feature selection process is essential to improve the authentication performance with fewer features to implement an efficient system in terms of computation power and energy consumption for a wearable device intended to be used as an authentication system. Support vector machines in conjunction with the optimized RL algorithm yielded accuracy outcomes using fewer features that were approximately 5%, 3.6%, and 2.6% higher than those associated with information gain (IG), ReliefF, and pure reinforcement learning structures, respectively. Additionally, the optimized RL yielded mostly lower equal error rate (EER) values than the other feature selection algorithms, with fewer selected features.