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

Takahiko Sakai; Taku Shoji; Noboru Yoshida; Kosuke Fukumori; Yuichi Tanaka; Toshihisa Tanaka

ICASSP 2020accepted0 citations

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

Takahiko Sakai, Taku Shoji, Noboru Yoshida, Kosuke Fukumori, Yuichi Tanaka, Toshihisa Tanaka

Abstract

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.

BibTeX

@inproceedings{icassp2020_scalpnetdetectio,
  title = {Scalpnet: Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG Using Convolutional Neural Networks},
  author = {Takahiko Sakai and Taku Shoji and Noboru Yoshida and Kosuke Fukumori and Yuichi Tanaka and Toshihisa Tanaka},
  booktitle = {ICASSP 2020},
  year = {2020}
}