An exploratory analysis of variance and volatility in epileptic electroencephalograms
Abstract
The electroencephalogram (EEG) is a physiological time series that measures electrical activity at different locations in the brain, and plays an important role in epilepsy research. Exploring the variance and/or volatility may yield insights for seizure prediction, seizure detection and seizure propagation/dynamics. Maximal Overlap Discrete Wavelet Transforms (MODWTs) and ARMA-GARCH models were used to determine variance and volatility characteristics of 66 channels for different states of an epileptic EEG – sleep, awake, sleep-to-awake and seizure. The wavelet variances, changes in wavelet variances and volatility half-lives for the four states were compared for possible differences between seizure and non-seizure channels. The half-lives of two of the three seizure channels were found to be shorter than all of the non-seizure channels, based on 95% CIs for the pre-seizure and awake signals. No discernible patterns were found the wavelet variances of the change points for the different signals.
Subject Area
Biostatistics|Neurosciences
Recommended Citation
Follis, Jack, "An exploratory analysis of variance and volatility in epileptic electroencephalograms" (2011). Texas Medical Center Dissertations (via ProQuest). AAI3490746.
https://digitalcommons.library.tmc.edu/dissertations/AAI3490746