{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:16:37Z","timestamp":1778876197132,"version":"3.51.4"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jbi.2026.105026","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:36:58Z","timestamp":1774283818000},"page":"105026","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia"],"prefix":"10.1016","volume":"178","author":[{"given":"Guangmeng","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4367-928X","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40345-019-0160-1","article-title":"Bipolar depression: a major unsolved challenge","volume":"8","author":"Baldessarini","year":"2020","journal-title":"Int. J. Bipolar Disord."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b2","article-title":"Mental health prediction using machine learning: taxonomy, applications, and challenges","volume":"2022","author":"Chung","year":"2022","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"10.1016\/j.jbi.2026.105026_b3","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.2147\/NDT.S273503","article-title":"Treatment-resistant bipolar depression: therapeutic trends, challenges and future directions","author":"Elsayed","year":"2022","journal-title":"Neuropsychiatr. Dis. Treat."},{"issue":"31","key":"10.1016\/j.jbi.2026.105026_b4","doi-asserted-by":"crossref","first-page":"9350","DOI":"10.12998\/wjcc.v9.i31.9350","article-title":"Major depressive disorder: Validated treatments and future challenges","volume":"9","author":"Karrouri","year":"2021","journal-title":"World J. Clin. Cases"},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1176\/appi.neuropsych.20100266","article-title":"The diagnostic challenge of young-onset dementia syndromes and primary psychiatric diseases: results from a retrospective 20-year cross-sectional study","volume":"34","author":"Tsoukra","year":"2022","journal-title":"J. Neuropsychiatry Clin. Neurosci."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b6","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/wps.21034","article-title":"New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications","volume":"22","author":"Andreassen","year":"2023","journal-title":"World Psychiatry"},{"key":"10.1016\/j.jbi.2026.105026_b7","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2023.1152366","article-title":"Exploring mental health challenges and coping strategies in university students during the COVID-19 pandemic: A case study in Dhaka city, Bangladesh","volume":"11","author":"Billah","year":"2023","journal-title":"Front. Public Health"},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b8","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1002\/wps.20825","article-title":"An organization-and category-level comparison of diagnostic requirements for mental disorders in ICD-11 and DSM-5","volume":"20","author":"First","year":"2021","journal-title":"World Psychiatry"},{"key":"10.1016\/j.jbi.2026.105026_b9","series-title":"Clinical Descriptions and Diagnostic Requirements for ICD-11 Mental, Behavioural and Neurodevelopmental Disorders","author":"World Health Organization","year":"2024"},{"key":"10.1016\/j.jbi.2026.105026_b10","series-title":"Encyclopedia of Gerontology and Population Aging","first-page":"1414","article-title":"Diagnostic and statistical manual of mental disorders-5 (DSM-5)","author":"Ma","year":"2022"},{"issue":"4","key":"10.1016\/j.jbi.2026.105026_b11","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1176\/appi.ps.202000830","article-title":"ICD-11 guidelines for mental and behavioral disorders of children and adolescents: reliability and clinical utility","volume":"73","author":"Robles","year":"2022","journal-title":"Psychiatr. Serv."},{"key":"10.1016\/j.jbi.2026.105026_b12","series-title":"Delving into the ICD-11: a review of the chapter on mental, behavioural or neurodevelopmental disorders","author":"Attard-Mallia","year":"2023"},{"key":"10.1016\/j.jbi.2026.105026_b13","series-title":"Handbook of Clinical Child Psychology: Integrating Theory and Research Into Practice","first-page":"53","article-title":"The WHO international classification of diseases 11th revision (ICD-11)","author":"Dawson-Squibb","year":"2023"},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b14","article-title":"Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study","volume":"11","author":"Anmella","year":"2023","journal-title":"JMIR MHealth UHealth"},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41398-020-01181-x","article-title":"A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data","volume":"11","author":"Tomasik","year":"2021","journal-title":"Transl. Psychiatry"},{"key":"10.1016\/j.jbi.2026.105026_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108959","article-title":"Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning","volume":"180","author":"Rykov","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b17","doi-asserted-by":"crossref","first-page":"18596","DOI":"10.1038\/s41598-023-44592-8","article-title":"Personalized relapse prediction in patients with major depressive disorder using digital biomarkers","volume":"13","author":"Vairavan","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b18","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s44277-024-00008-7","article-title":"Increasing psychopharmacology clinical trial success rates with digital measures and biomarkers: Future methods","volume":"2","author":"Reiter","year":"2024","journal-title":"NPP\u2014Digit. Psychiatry Neurosci."},{"issue":"10","key":"10.1016\/j.jbi.2026.105026_b19","doi-asserted-by":"crossref","first-page":"957","DOI":"10.3390\/jpm11100957","article-title":"Machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions","volume":"11","author":"Richter","year":"2021","journal-title":"J. Pers. Med."},{"issue":"11","key":"10.1016\/j.jbi.2026.105026_b20","doi-asserted-by":"crossref","first-page":"e816","DOI":"10.1016\/S2589-7500(22)00152-2","article-title":"The promise of a model-based psychiatry: building computational models of mental ill health","volume":"4","author":"Hauser","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.jbi.2026.105026_b21","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2025.1595243","article-title":"The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review","volume":"7","author":"Garz\u00f3n-Partida","year":"2025","journal-title":"Front. Digit. Health"},{"key":"10.1016\/j.jbi.2026.105026_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.pnpbp.2024.111172","article-title":"Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders","volume":"136","author":"Song","year":"2025","journal-title":"Prog. Neuropsychopharmacol. Biol. Psychiatry"},{"issue":"8","key":"10.1016\/j.jbi.2026.105026_b23","doi-asserted-by":"crossref","first-page":"919","DOI":"10.3390\/life14080919","article-title":"The Brain\u2013Heart Axis: An Umbrella review on impact of psychiatric disease on incidence, management, and outlook of cardiovascular disease","volume":"14","author":"Mazza","year":"2024","journal-title":"Life"},{"key":"10.1016\/j.jbi.2026.105026_b24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jad.2024.03.098","article-title":"The brain-heart axis: Integrative analysis of the shared genetic etiology between neuropsychiatric disorders and cardiovascular disease","volume":"355","author":"Chen","year":"2024","journal-title":"J. Affect. Disord."},{"issue":"8","key":"10.1016\/j.jbi.2026.105026_b25","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.3390\/biomedicines12081719","article-title":"Heart\u2013Brain Axis: A narrative review of the interaction between depression and arrhythmia","volume":"12","author":"Fang","year":"2024","journal-title":"Biomedicines"},{"issue":"2","key":"10.1016\/j.jbi.2026.105026_b26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s00059-022-05104-y","article-title":"Mind and body interventions in cardiology: The importance of the brain\u2013heart connection","volume":"47","author":"Lurz","year":"2022","journal-title":"Herz"},{"issue":"6648","key":"10.1016\/j.jbi.2026.105026_b27","doi-asserted-by":"crossref","first-page":"abn6598","DOI":"10.1126\/science.abn6598","article-title":"Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images","volume":"380","author":"Zhao","year":"2023","journal-title":"Science"},{"issue":"8","key":"10.1016\/j.jbi.2026.105026_b28","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1093\/brain\/aww143","article-title":"Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders","volume":"139","author":"Zhang","year":"2016","journal-title":"Brain"},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b29","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11571-022-09918-8","article-title":"A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals","volume":"18","author":"Tasci","year":"2024","journal-title":"Cogn. Neurodynamics"},{"key":"10.1016\/j.jbi.2026.105026_b30","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1215097","article-title":"Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies","volume":"17","author":"Constable","year":"2023","journal-title":"Front. Neurosci."},{"issue":"3","key":"10.1016\/j.jbi.2026.105026_b31","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1093\/schbul\/sbae150","article-title":"EEG-based signatures of schizophrenia, depression, and aberrant aging: a supervised machine learning investigation","volume":"51","author":"Sarisik","year":"2025","journal-title":"Schizophr. Bull."},{"key":"10.1016\/j.jbi.2026.105026_b32","series-title":"2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","first-page":"651","article-title":"Ai ML enabled wearable smart sensors detecting psychological disorders","author":"Sharma","year":"2023"},{"key":"10.1016\/j.jbi.2026.105026_b33","doi-asserted-by":"crossref","first-page":"521","DOI":"10.3389\/fnhum.2018.00521","article-title":"EEG frequency bands in psychiatric disorders: a review of resting state studies","volume":"12","author":"Newson","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"10.1016\/j.jbi.2026.105026_b34","doi-asserted-by":"crossref","DOI":"10.1109\/TASE.2025.3555559","article-title":"Sim2real learning with domain randomization for autonomous guidewire navigation in robotic-assisted endovascular procedures","author":"Yao","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.jbi.2026.105026_b35","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2025.1584299","article-title":"A multi-modal deep learning approach for stress detection using physiological signals: integrating time and frequency domain features","volume":"16","author":"Xiang","year":"2025","journal-title":"Front. Physiol."},{"key":"10.1016\/j.jbi.2026.105026_b36","first-page":"1","article-title":"Deep learning-based automated emotion recognition using multimodal physiological signals and time-frequency methods","volume":"73","author":"Kumar","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.jbi.2026.105026_b37","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2025.1486763","article-title":"An emotion recognition method based on frequency-domain features of PPG","volume":"16","author":"Zhu","year":"2025","journal-title":"Front. Physiol."},{"issue":"10","key":"10.1016\/j.jbi.2026.105026_b38","doi-asserted-by":"crossref","first-page":"5718","DOI":"10.1109\/JBHI.2024.3403109","article-title":"Frequency domain deep learning with non-invasive features for intraoperative hypotension prediction","volume":"28","author":"Moon","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.jbi.2026.105026_b39","doi-asserted-by":"crossref","first-page":"66726","DOI":"10.1109\/ACCESS.2024.3399114","article-title":"Signal to image conversion and convolutional neural networks for physiological signal processing: A review","volume":"12","author":"Vidyasagar","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.jbi.2026.105026_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107772","article-title":"A multi-modal driver emotion dataset and study: Including facial expressions and synchronized physiological signals","volume":"130","author":"Xiang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"13","key":"10.1016\/j.jbi.2026.105026_b41","doi-asserted-by":"crossref","first-page":"12417","DOI":"10.1007\/s11042-024-19467-3","article-title":"Federated learning in emotion recognition systems based on physiological signals for privacy preservation: a review","volume":"84","author":"Gahlan","year":"2025","journal-title":"Multimedia Tools Appl."},{"issue":"S1","key":"10.1016\/j.jbi.2026.105026_b42","first-page":"317","article-title":"Nutzung der EKG-Signaldatenbank CARDIODAT der PTB \u00fcber das Internet","volume":"40","author":"Bousseljot","year":"1995","journal-title":"Biomed. Tech. \/ Biomed. Eng."},{"issue":"23","key":"10.1016\/j.jbi.2026.105026_b43","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.jbi.2026.105026_b44","article-title":"MIMIC-IV-ECG: Diagnostic electrocardiogram matched subset","author":"Gow","year":"2023","journal-title":"PhysioNet"},{"key":"10.1016\/j.jbi.2026.105026_b45","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority Over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artificial Intelligence Res."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b46","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11571-022-09918-8","article-title":"A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals","volume":"18","author":"Tasci","year":"2024","journal-title":"Cogn. Neurodynamics"},{"issue":"11","key":"10.1016\/j.jbi.2026.105026_b47","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/ad00ff","article-title":"ECGPsychNet: an optimized hybrid ensemble model for automatic detection of psychiatric disorders using ECG signals","volume":"44","author":"Khare","year":"2023","journal-title":"Physiol. Meas."},{"key":"10.1016\/j.jbi.2026.105026_b48","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.2147\/NDT.S429592","article-title":"Heart rate variability in psychiatric disorders: A systematic review","author":"Ramesh","year":"2023","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"10.1016\/j.jbi.2026.105026_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105120","article-title":"Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals","volume":"140","author":"Koh","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.jbi.2026.105026_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106005","article-title":"ECG-based biometric under different psychological stress states","volume":"202","author":"Zhou","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"issue":"7","key":"10.1016\/j.jbi.2026.105026_b51","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/pcn.13356","article-title":"Heart rate variability in patients with anxiety disorders: A systematic review and meta-analysis","volume":"76","author":"Cheng","year":"2022","journal-title":"Psychiatry Clin. Neurosci."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b52","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1093\/schbul\/sbab080","article-title":"Imbalance model of heart rate variability and pulse wave velocity in psychotic and nonpsychotic disorders","volume":"48","author":"Zhang","year":"2022","journal-title":"Schizophr. Bull."},{"key":"10.1016\/j.jbi.2026.105026_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.medengphy.2024.104275","article-title":"A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks","volume":"135","author":"Telangore","year":"2025","journal-title":"Med. Eng. Phys."},{"key":"10.1016\/j.jbi.2026.105026_b54","article-title":"Rofed-LLM: Robust federated learning for large language models in adversarial wireless environments","author":"Wang","year":"2025","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"1","key":"10.1016\/j.jbi.2026.105026_b55","article-title":"Machine learning, deep learning, and data preprocessing techniques for detecting, predicting, and monitoring stress and stress-related mental disorders: scoping review","volume":"11","author":"Razavi","year":"2024","journal-title":"JMIR Ment. Health"},{"key":"10.1016\/j.jbi.2026.105026_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105120","article-title":"Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals","volume":"140","author":"Koh","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.jbi.2026.105026_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107775","article-title":"Deep neural network technique for automated detection of ADHD and CD using ECG signal","volume":"241","author":"Loh","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"10.1016\/j.jbi.2026.105026_b58","doi-asserted-by":"crossref","first-page":"459","DOI":"10.9758\/cpn.2019.17.4.459","article-title":"Heart and brain interaction of psychiatric illness: a review focused on heart rate variability, cognitive function, and quantitative electroencephalography","volume":"17","author":"Jung","year":"2019","journal-title":"Clin. Psychopharmacol. Neurosci."},{"issue":"29","key":"10.1016\/j.jbi.2026.105026_b59","doi-asserted-by":"crossref","first-page":"73329","DOI":"10.1007\/s11042-023-17504-1","article-title":"Automated detection of mental disorders using physiological signals and machine learning: A systematic review and scientometric analysis","volume":"83","author":"Singh","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.jbi.2026.105026_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112334","article-title":"Anfis-moh: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms","volume":"167","author":"Wang","year":"2024","journal-title":"Appl. Soft Comput."}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S153204642600050X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S153204642600050X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T19:52:07Z","timestamp":1778874727000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S153204642600050X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":60,"alternative-id":["S153204642600050X"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2026.105026","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2026.105026","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105026"}}