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Interictal spike classification in pharmacoresistant epilepsy using combined EEG and MEG

Sdoukopoulou Glykeria, Antonakakis Marios, Moddel Gabriel, Wolters Carsten H., Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/2829DDC4-2559-422B-9141-4CFFC0739417-
Identifierhttps://doi.org/10.1109/BIBE52308.2021.9635501-
Identifierhttps://ieeexplore.ieee.org/document/9635501-
Languageen-
Extent6 pagesen
TitleInterictal spike classification in pharmacoresistant epilepsy using combined EEG and MEGen
CreatorSdoukopoulou Glykeriaen
CreatorΣδουκοπουλου Γλυκεριαel
CreatorAntonakakis Mariosen
CreatorΑντωνακακης Μαριοςel
CreatorModdel Gabrielen
CreatorWolters Carsten H.en
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryEpilepsy is one of the most common brain disorders worldwide. The basic principle in epilepsy is to resect the epileptogenic zone (EZ) when the medicaments are inadequate to suppress epileptic seizures. Epilepsy is accompanied by interictal spikes, a surrogate marker serving as an identifier of seizures. The automatic temporal detection of these spikes is of major importance due to the demanding time consumption of the manual annotation. Electro- and magneto- encephalography (EEG and MEG) are the most usual measurement modalities for the recording of brain activity. EEG and MEG are ideal modalities for the non-invasive monitoring of drug-resistant epilepsy. Many approaches have been proposed for the temporal detection of interictal spikes. However, only single measurement modality (EEG or MEG) has been used up to now, neglecting their complementary content. In this study, we develop a multi-feature and iterative classification scheme with input from either single modality (EEG or MEG) or combined EEG/MEG (EMEG). The inputs include statistical (kurtosis and Renyi Entropy) and spectral (Energy) features as well as the functional connectivity metrics, global and local efficiency from imaginary phase lag index networks. The classification performance for all modalities ranges from 89% to 92.8%, with the maximum performance being observed for EMEG. Overall, the complementarity of EEG and MEG on the detection of interictal spikes is promising, opening new considerations on the development of automatic epileptic spike detection approaches.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-12-
Date of Publication2021-
SubjectEpilepsyen
SubjectAutomatic spike detectionen
SubjectSVMen
SubjectEEGen
SubjectMEGen
SubjectClassificationen
Bibliographic CitationG. Sdoukopoulou, M. Antonakakis, G. Moddel, C. H. Wolters and M. Zervakis, "Interictal spike classification in pharmacoresistant epilepsy using combined EEG and MEG," presented at the 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE), Kragujevac, Serbia, 2021, doi: 10.1109/BIBE52308.2021.9635501.en

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