The viability of high-frequency oscillation analysis in EEG signals for seizure prediction

Bryan David Kern, University of Texas at El Paso

Abstract

Seizure prediction is a decades-old research problem that has yet to reach any satisfying conclusions. Many studies have tackled the problem with varying results, but there has yet to be any major breakthroughs that define the direction of all future research. However, a promising, new predictor may have presented itself in recent years in the form of high frequency oscillations (HFOs). With the discovery of HFOs (80–800 Hz) as a biomarker for epilepsy, new interest has been placed in studying the high frequency content of EEGs to find a possible link between epilepsy and HFOs. In this paper, we attempt to strengthen the connection between seizure onset and HFOs by analyzing the prevalence of HFOs during several brain states. Using the Montreal Neurological Institute (MNI) detector for HFO detection, we compared the rates of HFOs immediately preceding seizure onset to rates found under normal brain conditions. Our results suggest a strong correlation between HFO rates and imminent seizure onset, indicating that HFOs would indeed be viable in seizure prediction methods.^

Subject Area

Neurosciences|Biomedical engineering|Electrical engineering

Recommended Citation

Kern, Bryan David, "The viability of high-frequency oscillation analysis in EEG signals for seizure prediction" (2016). ETD Collection for University of Texas, El Paso. AAI10249766.
http://digitalcommons.utep.edu/dissertations/AAI10249766

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