Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG
| Autor: | Qibin Zhao, Toshihisa Tanaka, Linfeng Sui, Jianting Cao, Xuyang Zhao |
|---|---|
| Přispěvatelé: | RIKEN Center for Advanced Intelligence Project [Tokyo] (RIKEN AIP), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Saitama Institute of Technology, Guangdong University of Technology, Tokyo University of Agriculture and Technology (TUAT), Juntendo University, Rhythm-Based Brain Information Processing Unit [Wako] (RIKEN CBS), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN)-RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Hangzhou Dianzi University (HDU), John MacIntyre, Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis, TC 12, WG 12.5 |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Normalization (statistics)
STFT Computer science business.industry iEEG Short-time Fourier transform Pattern recognition 02 engineering and technology Epileptic Convolutional neural network Resection 03 medical and health sciences Surgical therapy 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Spectrogram Focus localization 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Artificial intelligence Epileptic foci business 030217 neurology & neurosurgery CNN |
| Zdroj: | IFIP Advances in Information and Communication Technology 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.331-339, ⟨10.1007/978-3-030-19823-7_27⟩ IFIP Advances in Information and Communication Technology ISBN: 9783030198220 AIAI |
| DOI: | 10.1007/978-3-030-19823-7_27⟩ |
| Popis: | Part 7: Deep Learning - Convolutional ANN; International audience; Epileptic focus localization is a critical factor for successful surgical therapy of resection of epileptogenic tissues. The key challenging problem of focus localization lies in the accurate classification of focal and non-focal intracranial electroencephalogram (iEEG). In this paper, we introduce a new method based on short time Fourier transform (STFT) and convolutional neural networks (CNN) to improve the classification accuracy. More specifically, STFT is employed to obtain the time-frequency spectrograms of iEEG signals, from which CNN is applied to extract features and perform classification. The time-frequency spectrograms are normalized with Z-score normalization before putting into this network. Experimental results show that our method is able to differentiate the focal from non-focal iEEG signals with an average classification accuracy of 91.8%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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