An SVM-based system and its performance for detection of seizures in neonates

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Abstract

This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance are reported. Additionally, a new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics.

Original languageEnglish
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEngineering the Future of Biomedicine, EMBC 2009
PublisherIEEE Computer Society
Pages2643-2646
Number of pages4
ISBN (Print)9781424432967
DOIs
Publication statusPublished - 2009
Event31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States
Duration: 2 Sep 20096 Sep 2009

Publication series

NameProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Conference

Conference31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Country/TerritoryUnited States
CityMinneapolis, MN
Period2/09/096/09/09

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