Title :
Modulated high frequency oscillations can identify regions of interest in human iEEG using hidden Markov models
Author :
Guirgis, Mirna ; Chinvarun, Yotin ; del Campo, Martin ; Carlen, Peter L. ; Bardakjian, Berj L.
Author_Institution :
Inst. of Biomater. & Biomed. Eng. (IBBME), Univ. of Toronto, Toronto, ON, Canada
Abstract :
This study investigated the seizure and non-seizure state transitions in the intracranial electroencephalogram (iEEG) recordings of extratemporal lobe epilepsy patients. Cross-frequency coupling between low and high frequency oscillations in conjunction with an unsupervised learning algorithm - namely, hidden Markov models - was used to objectively identify seizure and non-seizure states as well as transition states. Channels consistently capturing two and/or three distinct states in a 32-channel iEEG array were able to identify regions of interest located in resected tissue of patients who experienced improved post-surgical outcomes.
Keywords :
electroencephalography; hidden Markov models; neurophysiology; unsupervised learning; cross-frequency coupling; extratemporal lobe epilepsy patients; hidden Markov models; human iEEG; intracranial electroencephalogram; modulated high frequency oscillations; nonseizure state transitions; seizure state; unsupervised learning algorithm; Computational modeling; Electrodes; Frequency modulation; Hidden Markov models; Oscillators; Training;
Conference_Titel :
Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on
Conference_Location :
Montpellier
DOI :
10.1109/NER.2015.7146777