Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks

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 convolutiona...

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Bibliographic Details
Main Authors: Sakai, Takahiko, Shoji, Taku, Yoshida, Noboru, Fukumori, Kosuke, Tanaka, Yuichi, Tanaka, Toshihisa
Format: Conference Proceeding
Language:English
Subjects:
Convolutional neural networks
deep neural networks
electroencephalogram (EEG)
Electroencephalography
Epilepsy
Neural networks
Signal processing
Spatiotemporal phenomena
Speech processing
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Description
Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9054705