Key result
Optimized reinforcement learning for ECG feature selection improves authentication accuracy up to ~5% versus standard methods.
Why the study?
The study investigated optimal features of ECG signals to implement an efficient personal authentication system using reinforcement learning for wearable devices.
Does an optimized reinforcement learning algorithm for ECG feature selection improve personal authentication accuracy compared to other feature selection methods?
Does an optimized reinforcement learning algorithm for ECG feature selection improve personal authentication accuracy compared to other feature selection methods?
An optimized deep reinforcement learning approach for ECG feature selection improves personal authentication accuracy while requiring fewer features, potentially enabling more efficient wearable authentication devices.
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.
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Baek et al. (2023) studied Personal authentication (n=11). Optimized reinforcement learning (RL) algorithm for feature selection vs. Information gain (IG), ReliefF, and pure reinforcement learning structures was evaluated on Authentication accuracy and equal error rate (EER). Support vector machines with an optimized reinforcement learning algorithm for ECG feature selection improved authentication accuracy by 5%, 3.6%, and 2.6% compared to IG, ReliefF, and pure RL.
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