{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:49:19Z","timestamp":1777942159987,"version":"3.51.4"},"reference-count":45,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p> Approximately, one third of patients with epilepsy are refractory to medical therapy and thus can be at high risk of injuries and sudden unexpected death. A low-complexity electroencephalography (EEG)-based seizure monitoring algorithm is critically important for daily use, especially for wearable monitoring platforms. This paper presents a personalized EEG feature selection approach, which is the key to achieve a reliable seizure monitoring with a low computational cost. We advocate a two-step, personalized feature selection strategy to enhance monitoring performances for each patient. In the first step, linear discriminant analysis (LDA) is applied to find a few seizure-indicative channels. Then in the second step, least absolute shrinkage and selection operator (LASSO) method is employed to select a discriminative subset of both frequency and time domain features (spectral powers and entropy). A personalization strategy is further customized to find the best settings (number of channels and features) that yield the highest classification scores for each subject. Experimental results of analyzing 23 subjects in CHB-MIT database are quite promising. We have achieved an average F-1 score of 88% with excellent sensitivity and specificity using not more than 7 features extracted from at most 3 channels. <\/jats:p>","DOI":"10.1142\/s0129065721500180","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T03:18:14Z","timestamp":1613531894000},"page":"2150018","source":"Crossref","is-referenced-by-count":37,"title":["Personalized EEG Feature Selection for Low-Complexity Seizure Monitoring"],"prefix":"10.1142","volume":"31","author":[{"given":"Genchang","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehrdad","family":"Nourani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jay","family":"Harvey","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurotherapeutic, The University of Texas Southwestern Medical Center, Dallas 75230, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hina","family":"Dave","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurotherapeutic, The University of Texas Southwestern Medical Center, Dallas 75230, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"S0129065721500180BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.886855"},{"key":"S0129065721500180BIB002","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-2007-14301"},{"key":"S0129065721500180BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2015.01.012"},{"key":"S0129065721500180BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.017"},{"key":"S0129065721500180BIB005","doi-asserted-by":"publisher","DOI":"10.1177\/1550059417744890"},{"key":"S0129065721500180BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2018.09.030"},{"key":"S0129065721500180BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2019.2920068"},{"key":"S0129065721500180BIB008","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500355"},{"key":"S0129065721500180BIB009","doi-asserted-by":"publisher","DOI":"10.1111\/epi.16719"},{"key":"S0129065721500180BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9048990"},{"key":"S0129065721500180BIB011","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065716500313"},{"key":"S0129065721500180BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3003733"},{"key":"S0129065721500180BIB013","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500173"},{"key":"S0129065721500180BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2019.8834648"},{"key":"S0129065721500180BIB015","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065719500217"},{"key":"S0129065721500180BIB016","first-page":"1","volume":"00","author":"Stirling R. 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