Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/2829DDC4-2559-422B-9141-4CFFC0739417 | - |
| Αναγνωριστικό | https://doi.org/10.1109/BIBE52308.2021.9635501 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/9635501 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | Interictal spike classification in pharmacoresistant epilepsy using combined EEG and MEG | en |
| Δημιουργός | Sdoukopoulou Glykeria | en |
| Δημιουργός | Σδουκοπουλου Γλυκερια | el |
| Δημιουργός | Antonakakis Marios | en |
| Δημιουργός | Αντωνακακης Μαριος | el |
| Δημιουργός | Moddel Gabriel | en |
| Δημιουργός | Wolters Carsten H. | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Epilepsy 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-05-12 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Epilepsy | en |
| Θεματική Κατηγορία | Automatic spike detection | en |
| Θεματική Κατηγορία | SVM | en |
| Θεματική Κατηγορία | EEG | en |
| Θεματική Κατηγορία | MEG | en |
| Θεματική Κατηγορία | Classification | en |
| Βιβλιογραφική Αναφορά | G. 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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