Title: Multistage preictal seizure analysis using Hidden Markov Model

Authors: Alan W.L. Chiu; Hareesh Gadi; Daniel W. Moller; Taufik A. Valiante; Danielle M. Andrade

Addresses: Applied Biology and Biomedical Engineering, Rose-Hulman Institute of Technology, 5500 Wabash Ave, Terre Haute, IN 47803, USA ' Biomedical Engineering, Louisiana Tech University, Ruston, LA 71270, USA ' Biomedical Engineering, Louisiana Tech University, Ruston, LA 71270, USA ' Toronto Western Research Institute, Krembil Neuroscience Center, University Health Network, University of Toronto, Toronto, Ontario, M5T2S8 Canada ' Toronto Western Research Institute, Krembil Neuroscience Center, University Health Network, University of Toronto, Toronto, Ontario, M5T2S8 Canada

Abstract: Epileptic seizures may be described as the population entrainment of low complexity activities. Multistage seizure detection implemented using hidden Markov model (HMM) was proposed for the analysis of intracranial EEG recordings of epilepsy patients. The number of hidden states and the number of Gaussian clusters of the HMM were obtained using an unsupervised method. Multiple dynamic stages had been found leading to ictal activity. High sensitivity and specificity can be achieved based on receiver operating characteristic curve analysis (with area > 0.85). Spatial generalisation features study suggested that HMM framework is independent of the subject and the testing recording location.

Keywords: epilepsy; early seizure detection; expert systems; HMM; hidden Markov model; preictal seizure analysis; epileptic seizures; intracranial EEG; electroencephalograms.

DOI: 10.1504/IJBET.2012.049366

International Journal of Biomedical Engineering and Technology, 2012 Vol.10 No.2, pp.160 - 173

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 01 Oct 2012 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article