{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:52:23Z","timestamp":1789671143018,"version":"4.0.1"},"reference-count":62,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia Open"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non\u2010invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS\u20102 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty\u2010four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist\u2010worn or chest\u2010worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n\u2009=\u200911) or a combination of HR and heart rate variability (HRV) (n\u2009=\u20096). HR\u2010based seizure detection studies collectively reported a sensitivity range of 56%\u2010100% and a false alarm rate (FAR) of 0.02\u20108\/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof\u2010of\u2010principle stage, and evidence for real\u2010time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large\u2010scale longitudinal data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Plain Language Summary<\/jats:title>\n                    <jats:p>This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real\u2010time. Therefore, more real\u2010time studies are needed to verify the usage of heart activity\u2010related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/epi4.12854","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T02:01:18Z","timestamp":1698285678000},"page":"41-59","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Feasibility of cardiac\u2010based seizure detection and prediction: A systematic review of non\u2010invasive wearable sensor\u2010based studies"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7827-0807","authenticated-orcid":false,"given":"Eryse Amira","family":"Seth","sequence":"first","affiliation":[{"name":"Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences Monash University Malaysia  Bandar Sunway Malaysia"},{"name":"Jeffrey Cheah School of Medicine and Health Sciences Monash University Malaysia  Bandar Sunway Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-0661","authenticated-orcid":false,"given":"Jessica","family":"Watterson","sequence":"additional","affiliation":[{"name":"Jeffrey Cheah School of Medicine and Health Sciences Monash University Malaysia  Bandar Sunway Malaysia"},{"name":"Department of Human\u2010Centred Computing Monash University  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-1627","authenticated-orcid":false,"given":"Jue","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Human\u2010Centred Computing Monash University  Melbourne Victoria 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