Efficient epileptic seizure detection based on electroencephalography signal

Ying Mei Qin, Chun Xiao Han, Yan Qiu Che, Hui Yan Li

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

1 Scopus citations


We investigate the detection of epileptic seizure onset based on electroencephalography (EEG) signal in short-time sessions (less than one second) from various samples from epilepsy and healthy people. Wavelet transform methods are applied to extract the features embedded in the high-dimensional epileptic EEG signal. It is found that results of wavelet transform play significant roles in dimensional reduction process. Then, machine learning pipeline is built based on support vector machine (SVM) algorithm. It is found that epileptic seizure state in the test data set could be predicted with high precision (98.1%) based only on mini-segments of EEG signal (0.6 second). Predictions based on 0.1 second mini-segments of EEG signal are also investigated. This research may be significant to the clinical treatment of epileptic seizure, because efficient methods could be applied to interrupt the process of epileptic seizure very fast (in less than one second).

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9789881563934
StatePublished - Sep 7 2017
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: Jul 26 2017Jul 28 2017

Publication series

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


Other36th Chinese Control Conference, CCC 2017

All Science Journal Classification (ASJC) codes

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


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