Discriminating between best performing features for seizure detection and data selection

Lojini Logesparan, Alexander J. Casson, Syed Anas Imtiaz, Esther Rodriguez-Villegas

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Seizure detection algorithms have been developed to solve specific problems, such as seizure onset detection, occurrence detection, termination detection and data selection. It is thus inherent that each type of seizure detection algorithm would detect a different EEG characteristic (feature). However most feature comparison studies do not specify the seizure detection problem for which their respective features have been evaluated. This paper shows that the best features/algorithm bases are not the same for all types of algorithms but depend on the type of seizure detection algorithm wanted. To demonstrate this, 65 features previously evaluated for online seizure data selection are re-evaluated here for seizure occurrence detection, using performance metrics pertinent to each seizure detection type whilst keeping the testing methodology the same. The results show that the best performing features/algorithm bases for data selection and occurrence detection algorithms are different and that it is more challenging to achieve high detection accuracy for the former seizure detection type. This paper also provides a comprehensive evaluation of the performance of 65 features for seizure occurrence detection to aid future researchers in choosing the best performing feature(s) to improve seizure detection accuracy. © 2013 IEEE.
    Original languageEnglish
    Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS
    Pages1692-1695
    Number of pages3
    DOIs
    Publication statusPublished - 2013
    Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka
    Duration: 1 Jul 2013 → …

    Conference

    Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
    CityOsaka
    Period1/07/13 → …

    Fingerprint

    Dive into the research topics of 'Discriminating between best performing features for seizure detection and data selection'. Together they form a unique fingerprint.

    Cite this