{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:38:28Z","timestamp":1763105908630,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"De Christelijke Vereniging voor de Verpleging van Lijders aan Epilepsie","award":["35401"],"award-info":[{"award-number":["35401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic\u2013clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system.<\/jats:p>","DOI":"10.3390\/s23020968","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Remote Sensing Paradigm for Real-Time Alerting of Convulsive Epileptic Seizures"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-7778","authenticated-orcid":false,"given":"Stiliyan","family":"Kalitzin","sequence":"first","affiliation":[{"name":"Stichting Epilepsie Instellingen Nederland (SEIN), 2103 SW Heemstede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1212\/WNL.0000000000003685","article-title":"Practice guideline summary: Sudden unexpected death in epilepsy incidence rates and risk factors: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology and the American Epilepsy Society","volume":"88","author":"Harden","year":"2017","journal-title":"Neurology"},{"key":"ref_2","first-page":"253","article-title":"Sudden unexpected death in epilepsy: People with nocturnal seizures may be at highest risk","volume":"23","author":"Lamberts","year":"2017","journal-title":"Epilepsia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107607","DOI":"10.1016\/j.yebeh.2020.107607","article-title":"Seizure detection devices: A survey of needs and preferences of paients and caregivers","volume":"114","author":"Assi","year":"2021","journal-title":"Epilepsy Behav."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S11","DOI":"10.1111\/epi.16521","article-title":"Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?","volume":"61","author":"Bruno","year":"2020","journal-title":"Epilepsia"},{"key":"ref_5","first-page":"3379","article-title":"Automatic Segmentation of Episodes Containing Epileptic Clonic Seizures in Video Sequences","volume":"59","author":"Kalitzin","year":"2012","journal-title":"TBME IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/epi.14050","article-title":"Automated video-based detection of nocturnal convulsive seizures in a residential care setting","volume":"59","author":"Geertsema","year":"2018","journal-title":"Epilepsia"},{"key":"ref_7","first-page":"S36","article-title":"Automated video-based detection of nocturnal motor seizures in children","volume":"61","author":"Westrhenen","year":"2020","journal-title":"Epilepsia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1002\/epi4.12076","article-title":"Multimodal, automated detection of nocturnal motorseizures at home: Is a reliable seizure detector feasible?","volume":"2","author":"Ungureanu","year":"2017","journal-title":"Epilepsia Open"},{"key":"ref_9","first-page":"290","article-title":"Optical flow group-parameter reconstruction from multi-channel image sequences","volume":"Volume 310","author":"Petkov","year":"2018","journal-title":"Frontiers of Artificial Intelligence and Applications Application of Intelligent Systems"},{"key":"ref_10","first-page":"302","article-title":"Scale-iterative optical flow reconstruction from multi-channel image sequences","volume":"Volume 310","author":"Petkov","year":"2018","journal-title":"Frontiers of Artificial Intelligence and Applications Application of Intelligent Systems"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1650027","DOI":"10.1142\/S0129065716500271","article-title":"Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse","volume":"26","author":"Kalitzin","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.neuroscience.2004.03.014","article-title":"Dynamics of non-convulsive epileptic phenomena modelled by a bistable neuronal network","volume":"126","author":"Suffczynski","year":"2004","journal-title":"Neuroscience"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101658","DOI":"10.1016\/j.bspc.2019.101658","article-title":"Automated non-contact detection of central apneas using video","volume":"55","author":"Geertsema","year":"2020","journal-title":"J. Neural Biomed. Signal Process. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jbiomech.2019.03.007","article-title":"Automated remote fall detection using impact features from video and audio","volume":"88","author":"Geertsema","year":"2019","journal-title":"J. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.1109\/JBHI.2021.3049649","article-title":"Video-based detection of generalized tonic-clonic seizures using deep learning","volume":"25","author":"Yang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/968\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:02Z","timestamp":1760119562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,14]]},"references-count":15,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020968"],"URL":"https:\/\/doi.org\/10.3390\/s23020968","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,14]]}}}