{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:00Z","timestamp":1747216140746,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685434"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:p>The rise of wearable EEG devices has opened the opportunity to develop new tools for neurological disorder monitoring, particularly for conditions like epilepsy. Machine learning plays a key role in processing the EEG signal towards an assessment of the person\u2019s state, and eventually evaluating some condition risk. However, existing approaches often rely on raw EEG data, keeping a numerical representation of the information contained in the data. Conversely, in a previous work, we explored representing the EEG signals using sequential patterns. In this work, we analyze the potential of such a representation through several machine learning methods, including decision trees, support vector machines, k-nearest neighbors, and random forest. The experiments carried out with the CHB-MIT scalp EEG database of Physionet show the outperformance of random forest.<\/jats:p>","DOI":"10.3233\/faia240410","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:45Z","timestamp":1727689665000},"source":"Crossref","is-referenced-by-count":0,"title":["Detection of Epileptic Seizures with EEG Sequential Patterns"],"prefix":"10.3233","author":[{"given":"Cristina","family":"Montserrat","sequence":"first","affiliation":[{"name":"eXiT research group, University of Girona"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1107-3163","authenticated-orcid":false,"given":"Jonah","family":"Fernandez","sequence":"additional","affiliation":[{"name":"eXiT research group, University of Girona"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1544-9658","authenticated-orcid":false,"given":"Bianca","family":"Innocenti","sequence":"additional","affiliation":[{"name":"eXiT research group, University of Girona"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9210-0073","authenticated-orcid":false,"given":"Beatriz","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"eXiT research group, University of Girona"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240410","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:45Z","timestamp":1727689665000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240410","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}