Automatic Detection of Seizures Using Extreme Learning Machine with a Single Feature

Yingmei Qin, Chunxiao Han, Meili Lu, Ruofan Wang, Li Yang, Yanqiu Che

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

1 Scopus citations


Automatic seizure detection is of great importance in clinical practice of epilepsy. This paper presents a classification system based on discrete wavelet transform (DWT) and the extreme learning machine (ELM) for epileptic seizure detection by distinguishing ictal and interictal electroencephalogram (EEG) signals. The original EEG signal is first decomposed by Daubechies order 4 wavelet into several sub-bands. Then, standard deviation, log of amplitude, and quartiles are calculated for the original and decomposed signals to construct feature vectors. Different combination of these features are fed into ELM and support vector machine (SVM). After comparing different combination strategies, we find that, using ELM, even with a single feature (standard deviation) from a single sub-band signal (4-8Hz), one can obtain a satisfactory classification result, which remarkably reduce the computational complexity and make the detection system more practical.

Original languageEnglish (US)
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9789881563941
StatePublished - Oct 5 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: Jul 25 2018Jul 27 2018

Publication series

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


Other37th Chinese Control Conference, CCC 2018

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Automatic Detection of Seizures Using Extreme Learning Machine with a Single Feature'. Together they form a unique fingerprint.

Cite this