Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/31313D48-A715-4BD6-B5FC-023143CC5D74 | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.compbiomed.2018.05.019 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S001048251830132X | - |
| Γλώσσα | en | - |
| Μέγεθος | 14 pages | en |
| Τίτλος | A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals | en |
| Δημιουργός | Tsiouris Κostas Μ. | en |
| Δημιουργός | Pezoulas Vasileios | en |
| Δημιουργός | Πεζουλας Βασιλειος | el |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Konitsiotis Spiros Th | en |
| Δημιουργός | Koutsourī́s, Dīmī́trios | en |
| Δημιουργός | Fotiadis, Dimitrios Ioannou | en |
| Εκδότης | Elsevier | en |
| Περίληψη | The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11–0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2019-08-28 | - |
| Ημερομηνία Δημοσίευσης | 2018 | - |
| Θεματική Κατηγορία | Deep learning | en |
| Θεματική Κατηγορία | EEG | en |
| Θεματική Κατηγορία | Epilepsy | en |
| Θεματική Κατηγορία | LSTM model | en |
| Θεματική Κατηγορία | Seizure prediction | en |
| Θεματική Κατηγορία | Electroencephalography | en |
| Θεματική Κατηγορία | Forecasting | en |
| Θεματική Κατηγορία | Neurophysiology | en |
| Βιβλιογραφική Αναφορά | K. M. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris and D. I. Fotiadis, "A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals," Comput. Biol. Med., vol. 99, pp. 24-37, Aug. 2018. doi: 10.1016/j.compbiomed.2018.05.019 | en |
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