Autoregressive spectral analysis of cortical electroencephalographic signals in a rat model of post-traumatic epilepsy

Abstract

Objectives:Post-traumatic epilepsy (PTE) is a common consequence of traumatic brain injury (TBI) and significant predictor of poor prognosis in TBI patients. To develop clinical interventions for PTE risk reduction, there is a need to elucidate the epileptogenic mechanisms induced by brain injury.Methods:The iron-induced rat model of epilepsy used here mimics many aspects of human PTE. Intracortical injection of iron results in local neuronal damage and the establishment of an epileptic focus, leading to chronic spontaneous electroencephalographic (EEG) signals and motor seizures, with progressively increasing frequency over many months. Identifying unique aspects of PTE seizure semiology for prognosis and treatment may be aided by novel methods of EEG analysis. Here, autoregressive (AR) methods were compared to the conventional fast Fourier transform (FFT) for processing EEG signals in iron-induced epilepsy.Results:Power spectra obtained using AR showed higher frequency resolution over a given epoch than the spectra obtained using FFT. Moreover, changes in total AR spectral power and frequency distribution over brief successive periods provided convenient indexes for long-term monitoring of seizures.Discussion:Autoregression analysis may prove complementary to FFT for EEG analysis in PTE patients.