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Discrimination of preictal and interictal brain states from long-term EEG data

Τσιουρής Κώστας Μ., Pezoulas Vasileios, Koutsouris, D, Zervakis Michail, Fotiadis, Dimitrios Ioannou

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/4FA5D9F9-5D85-42EC-93BB-F6495B54CE4F-
Αναγνωριστικόhttp://ieeexplore.ieee.org/document/8104210/-
Αναγνωριστικόhttps://doi.org/10.1109/CBMS.2017.33-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςDiscrimination of preictal and interictal brain states from long-term EEG dataen
ΔημιουργόςΤσιουρής Κώστας Μ.el
ΔημιουργόςTsiouris Κostas Μ.en
ΔημιουργόςPezoulas Vasileiosen
ΔημιουργόςΠεζουλας Βασιλειοςel
ΔημιουργόςKoutsouris, Den
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςFotiadis, Dimitrios Ioannouen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηThe discrimination of the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers. The analysis covers a patient-specific approach, so as to optimize the decision to each patient individually and a patient-independent approach in order to explore a global prediction approach that can discriminate randomly selected preictal and interictal segments from all patients. Overall, the first approach aims at revealing patient-specific epileptic characteristics, whereas the second seeks for potential general preictal-related signs. The results reveal that in the patient-specific case, the SVM classifier exhibits the highest classification accuracy in both preictal and interictal classes reaching 85.75% sensitivity and specificity. As it is expected, the classification performance is lower for the patient-independent case at 68.5%, due to the complicated nature of preictal activity and the variations among patients condition.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-03-16-
Ημερομηνία Δημοσίευσης2017-
Θεματική ΚατηγορίαClassificationen
Θεματική ΚατηγορίαEEGen
Θεματική ΚατηγορίαEpileptic Seizure Predictionen
Θεματική ΚατηγορίαInterictalen
Θεματική ΚατηγορίαPreictalen
Βιβλιογραφική ΑναφοράK. M. Tsiouris, V. C. Pezoulas, D. D. Koutsouris, M. Zervakis and D. I. Fotiadis, "Discrimination of preictal and interictal brain states from long-term EEG data," in 30th IEEE International Symposium on Computer-Based Medical Systems, 2017, pp. 318-323. doi:10.1109/CBMS.2017.33en

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