Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
| Autor: | Jack L. Follis, Dejian Lai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
time series characterization
0301 basic medicine Heteroscedasticity medicine.diagnostic_test business.industry Autoregressive conditional heteroskedasticity GARCH models Pattern recognition Electroencephalography medicine.disease Confidence interval Temporal lobe 03 medical and health sciences Epilepsy 030104 developmental biology 0302 clinical medicine Autoregressive model medicine epilepsy EEG Artificial intelligence Volatility (finance) business 030217 neurology & neurosurgery Mathematics |
| Zdroj: | Signals Volume 1 Issue 1 Pages 3-46 |
| ISSN: | 2624-6120 |
| DOI: | 10.3390/signals1010003 |
| Popis: | Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average&ndash Generalized Autoregressive Conditional Heteroscedasticity (ARMA&ndash GARCH) models confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process. |
| Databáze: | OpenAIRE |
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