{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T07:43:36Z","timestamp":1784619816667,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.<\/jats:p>","DOI":"10.3390\/s21237916","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7916","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals"],"prefix":"10.3390","volume":"21","author":[{"given":"Mingu","family":"Kang","sequence":"first","affiliation":[{"name":"AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-7856","authenticated-orcid":false,"given":"Siho","family":"Shin","sequence":"additional","affiliation":[{"name":"AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gengjia","family":"Zhang","sequence":"additional","affiliation":[{"name":"AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaehyo","family":"Jung","sequence":"additional","affiliation":[{"name":"AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youn Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1146\/annurev-psych-062520-122331","article-title":"Stress and health: A review of psychobiological processes","volume":"72","author":"Thayer","year":"2021","journal-title":"Annu. 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