Patient-specific Seizure Prediction with Scalp EEG Using Convolutional Neural Network and Extreme Learning Machine

Yingmei Qin, Hailing Zheng, Wei Chen, Qing Qin, Chunxiao Han, Yanqiu Che

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Seizure prediction can allow timely preventive measures for patients with epilepsy. In this study, we propose a hybrid model consisting of convolutional neural networks (CNNs) and an extreme learning machine (ELM) to predict seizures using scalp EEG. We first covert the EEG time series on 30-s windows into 2D spectrograms using the short-time Fourier transform. Then we apply CNNs to these images to extract features automatically. Finally, we use the ELM to classify preictal and interictal segments. The proposed method achieves sensitivity of 95.85% and a false prediction rate of 0.045/h on the Boston Children's Hospital-MIT scalp EEG dataset.

Original languageEnglish (US)
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Pages7622-7625
Number of pages4
ISBN (Electronic)9789881563903
DOIs
StatePublished - Jul 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: Jul 27 2020Jul 29 2020

Publication series

NameChinese Control Conference, CCC
Volume2020-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference39th Chinese Control Conference, CCC 2020
CountryChina
CityShenyang
Period7/27/207/29/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Applied Mathematics
  • Modeling and Simulation

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