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Probability-Based Approach for Epileptic Seizure Detection Using Hidden Markov Model

  • Deba Prasad DashEmail author
  • Maheshkumar H. Kolekar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)

Abstract

Seizure is defined as a sudden synchronous activity of a group of neurons resulting in an electric surge in the brain. Epilepsy is a brain disorder indicated by repeated seizures. Around 10 million people in India are suffering from epilepsy. Electroencephalogram (EEG) signal being low cost and non-invasive in nature can be used effectively for seizure detection. The present work focuses on developing an efficient epileptic seizure detection system using intracranial EEG signals. Dual tree complex wavelet transform is used to decompose the signal into various sub-frequency bands. Probability features are used to extract efficient indicators for seizure and healthy classes. Discriminant correlation analysis is used to increase the difference between different classes as well as reduce the difference between same classes. The fused feature set is clustered using fuzzy c means clustering algorithm. Hidden Markov model discriminates the seizure class with healthy class with good efficiency. Maximum accuracy of 98.57% is achieved for seizure detection with very low execution time.

Keywords

EEG Epilepsy Seizure Dual tree complex wavelet transform Discriminant correlation analysis Hidden Markov Model 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology PatnaPatnaIndia

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