Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/4FA5D9F9-5D85-42EC-93BB-F6495B54CE4F | - |
| Αναγνωριστικό | http://ieeexplore.ieee.org/document/8104210/ | - |
| Αναγνωριστικό | https://doi.org/10.1109/CBMS.2017.33 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | Discrimination of preictal and interictal brain states from long-term EEG data | en |
| Δημιουργός | Τσιουρής Κώστας Μ. | el |
| Δημιουργός | Tsiouris Κostas Μ. | en |
| Δημιουργός | Pezoulas Vasileios | en |
| Δημιουργός | Πεζουλας Βασιλειος | el |
| Δημιουργός | Koutsouris, D | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Fotiadis, Dimitrios Ioannou | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | The 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-03-16 | - |
| Ημερομηνία Δημοσίευσης | 2017 | - |
| Θεματική Κατηγορία | Classification | en |
| Θεματική Κατηγορία | EEG | en |
| Θεματική Κατηγορία | Epileptic Seizure Prediction | en |
| Θεματική Κατηγορία | Interictal | en |
| Θεματική Κατηγορία | Preictal | en |
| Βιβλιογραφική Αναφορά | K. 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.2017.33 | en |
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