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Classification of Seizure EEGs Based on Short-Time Fourier Transform and Hidden Markov Model

  • Harbin Institute of Technology
  • CAS - Suzhou Institute of Biomedical Engineering and Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Epilepsy is a kind of disorder that has affected many people in the world. Electroencephalogram (EEG) is an effective tool in the diagnosis and treatment of epilepsy. The classification of EEG signals from different seizure stages is of great interest in this field. This paper proposes a seizure EEG classification method based on Short-Time Fourier Transform (STFT) and Hidden Markov Model (HMM). We construct feature sequences by STFT, and then 50% of the sequences are used to train HMMs. Finally, the other sequences are used to evaluate the HMMs. Experiments conducted on the dataset from University of Bonn are provided, with the accuracy for set D and set E reaching 97.18%, and the sensitivity and specificity reaching 98.54% and 95.82% respectively.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages875-880
Number of pages6
ISBN (Electronic)9789881476883
StatePublished - 7 Dec 2020
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 7 Dec 202010 Dec 2020

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period7/12/2010/12/20

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