{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:42:46Z","timestamp":1781973766636,"version":"3.54.5"},"reference-count":71,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES)","doi-asserted-by":"publisher","award":["0001"],"award-info":[{"award-number":["0001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"APC payment and doctoral scholarships","award":["0001"],"award-info":[{"award-number":["0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models\u2014without resorting to deep learning\u2014is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.<\/jats:p>","DOI":"10.3390\/s23249871","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6146-5288","authenticated-orcid":false,"given":"Jusciaane Chacon","family":"Vieira","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Automation\u2014DCA, Federal University of Rio Grande do Norte\u2014UFRN, Natal 59078-900, RN, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2690-1563","authenticated-orcid":false,"given":"Luiz Affonso","family":"Guedes","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation\u2014DCA, Federal University of Rio Grande do Norte\u2014UFRN, Natal 59078-900, RN, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-131X","authenticated-orcid":false,"given":"Mailson Ribeiro","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation\u2014DCA, Federal University of Rio Grande do Norte\u2014UFRN, Natal 59078-900, RN, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3885-9510","authenticated-orcid":false,"given":"Ignacio","family":"Sanchez-Gendriz","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation\u2014DCA, Federal University of Rio Grande do Norte\u2014UFRN, Natal 59078-900, RN, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023, June 03). 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