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

Abstract

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.

Suwhan Baek

Researcher, Posco Holdings

His research interests include Medical AI, Auto ML, reinforcement learning, generative models, and SNN.

Juhyeong Kim

Researcher, WOORI BANK

His research interests include machine learning algorithms, specifically deep learning, reinforcement learning, and automated machine learning (AutoML).

Hyunsoo Yu

Researcher, LG Innotek

His research interests include experiment setting, signal processing, machine learning and artificial intelligence.

Geunbo Yang

Researcher, KG Steel

His research interests include machine learning algorithms and spiking neural networks.

Cheolsoo Park

Professor

His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.