{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:46:29Z","timestamp":1789991189656,"version":"4.0.1"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T00:00:00Z","timestamp":1566518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists\u2014a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.<\/jats:p>","DOI":"10.3390\/a12090176","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T04:38:23Z","timestamp":1566794303000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Online EEG Seizure Detection and Localization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9014-5024","authenticated-orcid":false,"given":"Amirsalar","family":"Mansouri","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, NE 68588-0511, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay P.","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine, Creighton University, Omaha, NE 68178, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8010-5272","authenticated-orcid":false,"given":"Khalid","family":"Sayood","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, NE 68588-0511, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1212\/01.wnl.0000252807.38124.a3","article-title":"How common are the common neurologic disorders?","volume":"68","author":"Hirtz","year":"2007","journal-title":"Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-94-017-8914-1_1","article-title":"How can we identify ictal and interictal abnormal activity? 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