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On-Chip Epilepsy Detection: Where Machine Learning Meets Patient-Specific Healthcare

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dc.contributor.authorYoo, Jerald-
dc.date.accessioned2024-05-03T04:33:36Z-
dc.date.available2024-05-03T04:33:36Z-
dc.date.created2024-05-01-
dc.date.issued2017-
dc.identifier.citationPROCEEDINGS INTERNATIONAL SOC DESIGN CONFERENCE 2017 (ISOCC 2017), pp.146-147-
dc.identifier.issn2163-9612-
dc.identifier.urihttps://hdl.handle.net/10371/200825-
dc.description.abstractDetecting epileptic seizure from EEG/iEEG is challenging due to each patient's onset pattern being very different from each other. To detect such onsets (in real time), the hardware must learn each patient's ictal pattern from the EEG/iEEG traces. This paper describes feature extraction and usage of Support Vector Machine (SVM)-based classifiers to achieve a patient-specific seizure detection System-on-Chip (SoC). Linear SVM (LSVM), Non-Linear SVM (NLSVM), and the Dual Detector Architecture (D(2)A)-SVM are described and compared. We show that when there are a limited number of training sets, the D(2)A-SVM classifier performs well while minimizing the hardware cost. The SoC is implemented and verified.-
dc.language영어-
dc.publisherIEEE-
dc.titleOn-Chip Epilepsy Detection: Where Machine Learning Meets Patient-Specific Healthcare-
dc.typeArticle-
dc.citation.journaltitlePROCEEDINGS INTERNATIONAL SOC DESIGN CONFERENCE 2017 (ISOCC 2017)-
dc.identifier.wosid000454793100068-
dc.identifier.scopusid2-s2.0-85048856695-
dc.citation.endpage147-
dc.citation.startpage146-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorYoo, Jerald-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorElectroencephalography (EEG)-
dc.subject.keywordAuthorepilepsy-
dc.subject.keywordAuthorinvasive EEG (iEEG)-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorseizure-
dc.subject.keywordAuthorsupport-vector machine (SVM)-
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부교수
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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