Paper Abstract and Keywords |
Presentation |
2019-03-15 13:30
[Poster Presentation]
Epileptic Focus Detection from Interictal Electroencephalogram using RNN Byambadorj Nyamradnaa, Kosuke Fukumori, Toshihisa Tanaka (TAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.) EA2018-138 SIP2018-144 SP2018-100 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Epilepsy is a neurological disorder characterized by recurrent seizures. An option of treatment is surgical resection of the brain tissue responsible for seizures. However, this option depends critically on accurate localization of the pathological brain tissue, which is referred to as the epileptic focus(seizure onset zone; SOZ). To this end, it is necessary to localize the SOZ causing seizures from the electrocorticogram (ECoG) signals. Since the inspection of ECoG recorded for a long time is a heavy load for clinicians, a technology for automatic detection of the epileptic focus from interictal ECoG is required. Therefore, We propose a method to detect epileptic focus using supervised learning from Interictal Electrocorticogram of epileptic patients. As a machine learning model, BiLSTM model which is one kind of Recurrent neural network is adopted. In addition, we compare performance with logistic regression model, SVM model as comparative models. When the epileptic focus was detected in a model learned for the interictal ECoG of a cortical dysplasia epilepsy patient, a high discrimination rate was achieved. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Epilepsy / Seizure onset zone / Electrocorticogram / Supervised learning / Recurrent Neural Network / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 496, SIP2018-144, pp. 229-231, March 2019. |
Paper # |
SIP2018-144 |
Date of Issue |
2019-03-07 (EA, SIP, SP) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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EA2018-138 SIP2018-144 SP2018-100 |
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