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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.<\/jats:p>","DOI":"10.3390\/s23031230","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5913-4471","authenticated-orcid":false,"given":"Suwhan","family":"Baek","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juhyeong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4750-6941","authenticated-orcid":false,"given":"Hyunsoo","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geunbo","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3943-4781","authenticated-orcid":false,"given":"Illsoo","family":"Sohn","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngho","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electrical and Communication Engineering, Daelim University, Kyoung 13916, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8042-007X","authenticated-orcid":false,"given":"Cheolsoo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jnca.2017.04.002","article-title":"Internet of Things security: A survey","volume":"88","author":"Alaba","year":"2017","journal-title":"J. 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