{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:55:36Z","timestamp":1782845736854,"version":"3.54.5"},"reference-count":33,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Panic attack prediction remains a critical challenge in mental health care due to the high interindividual variability of physiological responses and the limitations of subjective psychological assessments.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to develop a multimodal deep learning framework that integrates real-time physiological signals from wearable electrocardiogram (ECG) monitors and psychological assessments to improve the accuracy of panic attack prediction.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We adapted the ConvNetQuake architecture, originally designed for seismic detection, to extract temporal patterns from ECG signals. The model was pretrained on the PTB-XL ECG dataset and fine-tuned using wearable ECG data collected from adult participants. In parallel, psychological profiles based on the\n Diagnostic and Statistical Manual of Mental Disorders, 4th Edition<\/jats:italic>\n criteria and Panic Disorder Severity Scale assessments were encoded as auxiliary inputs. The multimodal framework was evaluated using standard performance metrics.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The proposed model achieved an accuracy of 71.43%, precision of 83.72%, recall of 70.59%, and F1 score of 76.60% in detecting heart rate variability anomalies associated with panic episodes. Experimental comparisons demonstrated that the integration of physiological and psychological modalities significantly outperformed unimodal baselines in prediction reliability.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study provides empirical support for wearable-based early warning systems for panic attacks. The proposed approach demonstrates the feasibility of just-in-time digital interventions and underscores the potential of wearable artificial intelligence in advancing affective computing and digital psychiatry.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/69045","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T14:45:06Z","timestamp":1753973106000},"page":"e69045-e69045","source":"Crossref","is-referenced-by-count":2,"title":["Panic Attack Prediction for Patients With Panic Disorder via Machine Learning and Wearable Electrocardiography Monitoring: Model Development and Validation Study"],"prefix":"10.2196","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-5138","authenticated-orcid":false,"given":"Hayoung","family":"Oh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8277-006X","authenticated-orcid":false,"given":"Hunmin","family":"Do","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0516-9422","authenticated-orcid":false,"given":"Chaehyun","family":"Maeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1631-590X","authenticated-orcid":false,"given":"Jinsuk","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7010-6038","authenticated-orcid":false,"given":"Taejun","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6258-0404","authenticated-orcid":false,"given":"Jihwan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5020-6687","authenticated-orcid":false,"given":"Hyeran","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3913-0940","authenticated-orcid":false,"given":"Seoin","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6986-8280","authenticated-orcid":false,"given":"Piao","family":"Huilin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"issue":"13","key":"R1","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1016\/0002-9149(94)90135-x","article-title":"A history of the origin, evolution, and impact of electrocardiography","volume":"73","author":"Fye","journal-title":"Am J Cardiol"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","DOI":"10.3402\/jchimp.v2i1.14383","article-title":"A brief review: history to understand fundamentals of electrocardiography","volume":"2","author":"AlGhatrif","journal-title":"J Community Hosp Intern Med Perspect"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108074","article-title":"A survey on wearable technology: history, state-of-the-art and current challenges","volume":"193","author":"Ometov","journal-title":"Comput Netw"},{"issue":"2","key":"R4","doi-asserted-by":"publisher","DOI":"10.3390\/s24020527","article-title":"The use of electrocardiogram smartwatches in patients with cardiac implantable electrical devices","volume":"24","author":"Strik","journal-title":"Sensors (Basel)"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.jacep.2022.09.011","article-title":"Clinical validation of 5 direct-to-consumer wearable smart devices to detect atrial fibrillation: BASEL wearable study","volume":"9","author":"Mannhart","journal-title":"JACC Clin Electrophysiol"},{"issue":"11","key":"R6","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/S0735-1097(20)31005-6","article-title":"Apple Watch Series 4 vs. 12-lead ECG: a comparison of manual electrocardiographic waveform analysis","volume":"75","author":"Saghir","journal-title":"J Am Coll Cardiol"},{"issue":"10","key":"R7","doi-asserted-by":"publisher","DOI":"10.3390\/s23104805","article-title":"Electrocardiogram monitoring wearable devices and artificial-intelligence-enabled diagnostic capabilities: a review","volume":"23","author":"Neri","journal-title":"Sensors (Basel)"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.836375","article-title":"Smartwatch electrocardiograms for automated and manual diagnosis of atrial fibrillation: a comparative analysis of three models","volume":"9","author":"Abu-Alrub","journal-title":"Front Cardiovasc Med"},{"issue":"9","key":"R9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2024.101208","article-title":"Anxiety and depression associated with increased cardiovascular disease risk through accelerated development of risk factors","volume":"3","author":"Civieri","journal-title":"JACC Adv"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2014.00080","article-title":"Anxiety disorders are associated with reduced heart rate variability: a meta-analysis","volume":"5","author":"Chalmers","journal-title":"Front Psychiatry"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2018.00735","article-title":"Heart rate variability as indicator of clinical state in depression","volume":"9","author":"Hartmann","journal-title":"Front Psychiatry"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2020.09.027","article-title":"Machine learning-based discrimination of panic disorder from other anxiety disorders","volume":"278","author":"Na","journal-title":"J Affect Disord"},{"issue":"2","key":"R13","doi-asserted-by":"publisher","DOI":"10.2196\/33063","article-title":"Panic attack prediction using wearable devices and machine learning: development and cohort study","volume":"10","author":"Tsai","journal-title":"JMIR Med Inform"},{"issue":"3","key":"R14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s40138-022-00248-x","article-title":"The use of wearable ECG devices in the clinical setting: a review","volume":"10","author":"Kamga","journal-title":"Curr Emerg Hosp Med Rep"},{"key":"R15","unstructured":"PulseNexa. Which smartwatches have FDA approval for ECG? Aug 12, 2024. URL: https:\/\/pulsenexa.com\/blogs\/news\/what-wearable-ecg-devices-are-fda- approved [Accessed 11-10-2024]"},{"issue":"13","key":"R16","doi-asserted-by":"publisher","DOI":"10.3390\/s24134201","article-title":"Wearable ECG device and machine learning for heart monitoring","volume":"24","author":"Alimbayeva","journal-title":"Sensors (Basel)"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.08.010","article-title":"Detection of mental fatigue state with wearable ECG devices","volume":"119","author":"Huang","journal-title":"Int J Med Inform"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108076","article-title":"Automated anxiety detection using probabilistic binary pattern with ECG signals","volume":"247","author":"Baygin","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","first-page":"105","DOI":"10.4218\/etrij.2021-0299","article-title":"Automated detection of panic disorder based on multimodal physiological signals using machine learning","volume":"45","author":"Jang","journal-title":"ETRI Journal"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1016\/j.sleep.2023.12.013","article-title":"Sleep, physical activity and panic attacks: a two-year prospective cohort study using smartwatches, deep learning and an explainable artificial intelligence model","volume":"114","author":"Tsai","journal-title":"Sleep Med"},{"key":"R21","unstructured":"Shaoxing People\u2019s HospitalNingbo First HospitalShaoxing and Ningbo First Hospital ECG DatabasePhysioNet\/CinC Challenge 2021. Kaggle. URL: https:\/\/www.kaggle.com\/datasets\/bjoernjostein\/shaoxing-and-ningbo-first-hospital-database [Accessed 19-09-2025]"},{"key":"R22","unstructured":"Carreiras C Alves AP Louren\u00e7o A et al. BioSPPy\u2014biosignal processing in Python. 2015. URL: https:\/\/github.com\/PIA-Group\/BioSPPy\/ [Accessed 11-11-2024]"},{"issue":"9","key":"R23","doi-asserted-by":"publisher","DOI":"10.2196\/12861","article-title":"Wearable health technology and electronic health record integration: scoping review and future directions","volume":"7","author":"Dinh-Le","journal-title":"JMIR Mhealth Uhealth"},{"issue":"1","key":"R24","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.5272","article-title":"Guidelines for developing and reporting machine learning predictive models in biomedical research","volume":"4","author":"Luo","journal-title":"JMIR Med Inform"},{"key":"R25","unstructured":"Lundberg SM Lee SI . A unified approach to interpreting model predictions. Presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc. 4768-4777; 2017.URL: https:\/\/papers.nips.cc\/paper_files\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html [Accessed 22-09-2025]"},{"key":"R26","doi-asserted-by":"crossref","unstructured":"Ribeiro MT Singh S Guestrin C . \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. Presented at: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 1135-1144; San Francisco, California, USA. [doi: 10.1145\/2939672.2939778]","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"R27","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/JBHI.2024.1234567","article-title":"Deep learning-based mood monitoring using wearable devices: a real-world application","volume":"28","author":"Gu","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"R28","doi-asserted-by":"publisher","first-page":"221","DOI":"10.5573\/ieek.2013.50.2.221","article-title":"Design of real-time vital-sign encryption module for wearable personal healthcare device","volume":"50","author":"Kim","journal-title":"Journal of the Institute of Electronics Engineers of Korea"},{"key":"R29","doi-asserted-by":"crossref","unstructured":"Lim J Koh Y Kim A Lee U . Exploring context-aware mental health self-tracking using multimodal smart speakers in home environments. Presented at: CHI \u201924. Association for Computing Machinery. 1-18; Honolulu HI USA. May 11, 2024.URL: https:\/\/dl.acm.org\/doi\/proceedings\/10.1145\/3613904 [doi: 10.1145\/3613904.3642846]","DOI":"10.1145\/3613904.3642846"},{"issue":"5","key":"R30","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/s0022-3956(01)00028-0","article-title":"Reliability and validity of the Panic Disorder Severity Scale: replication and extension","volume":"35","author":"Shear","journal-title":"J Psychiatr Res"},{"key":"R31","unstructured":"American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. Fourth Edition (DSM-IV) ed. American Psychiatric Association; 1994."},{"key":"R32","unstructured":"Google Drive \u2013 BeatSaver-Android-master.zip (Application Code, UI and System Integration). URL: https:\/\/drive.google.com\/file\/d\/131a6IofjgH1SmGU6V7hXwwZeO_PWmTqp\/view?usp=sharing [Accessed 22-09-2025]"},{"key":"R33","unstructured":"Google Drive \u2013 DataPreprocessing-main.zip (Python Code for Preprocessing and Modeling). URL: https:\/\/drive.google.com\/file\/d\/1TjR8o5FGUcfRgg38yjjCsWGkq3sVqPOo\/view?usp=sharing [Accessed 22-09-2025]"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2025\/1\/e69045","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:56:20Z","timestamp":1782842180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2025\/1\/e69045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":33,"URL":"https:\/\/doi.org\/10.2196\/69045","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"article-number":"v27i13e69045"}}