{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:03:24Z","timestamp":1790960604413,"version":"4.1.0"},"reference-count":115,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Heart rate variability (HRV) refers to variations in the time intervals between consecutive heart beats. Changes in HRV reflect changes in either sympathetic or decreased parasympathetic tone that can originate in the brain. This brain\u2013heart connection has led to the proposal that HRV may have utility in the diagnosis of psychiatric conditions and\/or be a predictor of the response to psychiatric medications. There have been attempts to improve the correlation between HRV and psychological and psychiatric conditions by using artificial intelligence or specific machine learning algorithms. The objective of this review is to synthesize data on the use of machine learning to improve accuracy in differentiating psychological conditions such as mental stress, as well as distinguishing persons with anxiety disorders, panic disorders, major depression disorders and schizophrenia from health subjects. Reported accuracies for the identification of mental stress vary from 42 to 94%, while accuracies for anxiety vary from 67 to 98%, panic disorders from 71 to 93% and depression from 71 to 95%. The ability of HRV to differentiate different psychological or psychiatric conditions from each other requires more investigation. The \u2018best\u2019 machine learning algorithm varied between studies, with some reporting the k-nearest neighbor algorithm, support vector machine, random forest, or neural networks to be the best. A number of studies combined HRV with other variables such as respiration, EEG, or electromyography to obtain a composite index, but in doing so obscured the independent contribution of HRV. In summary, HRV has shown promise in detecting abnormalities in a range of psychological and psychiatric conditions. The use of machine learning algorithms improves diagnostic accuracy.<\/jats:p>","DOI":"10.3390\/bioengineering13050554","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:07:26Z","timestamp":1779206846000},"page":"554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence in the Assessment of Heart Rate Variability as an Instrument to Understand the Connection Between Psychologic and Psychiatric Conditions and the Heart"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8923-1152","authenticated-orcid":false,"given":"Simon W.","family":"Rabkin","sequence":"first","affiliation":[{"name":"Division of Cardiology, Department of Medicine, University of British Columbia, 9th Floor 2775 Laurel St., Vancouver, BC V5Z 1M9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Namazi, A., Modiri, E., Blesi\u0107, S., Kne\u017eevi\u0107, O.M., and Mirkov, D.M. 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