Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks
| Autor: | Noboru Yoshida, Toshihisa Tanaka, Taku Shoji, Takahiko Sakai, Yuichi Tanaka, Kosuke Fukumori |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
medicine.diagnostic_test Computer science business.industry 0206 medical engineering Pattern recognition 02 engineering and technology Electroencephalography medicine.disease 020601 biomedical engineering Convolutional neural network 03 medical and health sciences Epilepsy 0302 clinical medicine medicine Epileptic eeg Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | ICASSP |
| Popis: | We propose ScalpNet: A deep neural network to detect spatiotemporal abnormal intervals from EEGs of epilepsy patients. Since the number of trained clinicians is very limited, it is very crucial to establish automatic detection of abnormal signals caused by epilepsy from EEGs. We build a convolutional neural network detecting spatiotemporal intervals that will be abnormal based on the fact that peaky EEG signals can be observed not only in the electrode close to the focal region but those in the surrounding regions. In the experiments with a real dataset, our proposed ScalpNet presents higher classification accuracy than existing machine learning methods, including a convolutional neural network performed by channel-by-channel. |
| Databáze: | OpenAIRE |
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