KESNER, Filip, Lukas SEKANINA and Milan BRÁZDIL. Modular framework for detection of inter-ictal spikes in iEEG. In 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017. New York: IEEE, 2017, p. 418-421. ISBN 978-1-5090-2809-2. Available from: https://dx.doi.org/10.1109/EMBC.2017.8036851.
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Basic information
Original name Modular framework for detection of inter-ictal spikes in iEEG
Authors KESNER, Filip (203 Czech Republic), Lukas SEKANINA (203 Czech Republic) and Milan BRÁZDIL (203 Czech Republic, guarantor, belonging to the institution).
Edition New York, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, p. 418-421, 4 pp. 2017.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 30210 Clinical neurology
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14110/17:00099906
Organization unit Faculty of Medicine
ISBN 978-1-5090-2809-2
ISSN 1557-170X
Doi http://dx.doi.org/10.1109/EMBC.2017.8036851
UT WoS 000427085300104
Keywords in English detection of inter-ictal spikes in iEEG
Tags EL OK
Tags International impact, Reviewed
Changed by Changed by: Mgr. Michal Petr, učo 65024. Changed: 28/4/2020 10:39.
Abstract
In this paper, we present a new modular approach for detection of inter-ictal spikes in intracranial iEEG recordings from patients that are suffering from pharmaco-resistant form of epilepsy. This new approach is presented in the form of a detection framework consisting of three primary modules: first level detector, second level feature extractor, and third level detection classifier, where each module is responsible for a specific functionality. This detection framework can be perceived as a three slot system, where modules can be easily plugged in their slots and replaced by a different module or implementation on demand, in order to adapt the quality of detection (measured in terms of sensitivity, precision or inter-recording adaptability) and computational cost. Using complex real-world data sets it was confirmed that the proposed framework provides highly sensitive and precise detection, while it also significantly reduces the computation time.
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