Power Spectral Analysis of EEG Characteristics During Sleep in Epileptics

Abstract

Power spectral analysis of spontaneous EEG activity recorded during non-rapid eye movement (REM) sleep was employed in an effort to identify characteristic EEG substrate abnormalities in epileptics. All-night polygraphic recordings were obtained from 10 poorly controlled subjects with mixed and generalized seizures. Similar data were obtained from 10 matched nonepileptic subjects. Continuous 10 min samples of non-REM sleep were identified at the beginning and end of these all-night recordings. EEG activity recorded during these periods from left sensorimotor and parieto-occipital bipolar leads was digitized, subjected to the fast Fourier transform, and then sorted into successive 4 Hz frequency bands between 0 and 23 Hz. Mean spectral density was determined for each frequency band from both samples in both groups. Comparison of these values disclosed both similarities and differences between the two groups. All subjects showed a significant "time-of-night effect," with power at 0-3, 4-7, and 8-11 Hz greater at the beginning of the night. This finding was related to a similar observation in developing infants and attributed to a circadian influence on neuronal generators. In group comparisons, epileptics showed selective differences in common when compared with nonepileptics. These differences were reduced when data from a small group of unmedicated epileptic subjects were partialled out. Medicated epileptics showed differences restricted to the early morning non-REM sleep sample, which included significantly greater power at 4-7 Hz and significantly lower power at 12-15 Hz. Unmedicated subjects showed these differences in both samples and also showed significantly greater power in the 0-3 Hz band. It was suggested that anticonvulsant medications may exert a general normalizing influence on EEG characteristics during sleep, particularly at the beginning of the night in proximity to their administration. The documentation of significant EEG deviations restricted to two basic rhythmic patterns during sleep suggests a disturbance of underlying thalamocortical mechanisms.

Does Greater Low Frequency EEG Activity in Normal Immaturity and in Children with Epilepsy Arise in the Same Neuronal Network?

“…In general, our findings of greater low frequency power in CE patients extend the results from EEG studies that found predominantly increased low frequency activity for welldefined forms of epilepsy (Clemens 2004a, b;Clemens et al 2000;2007a;Kobayashi et al 2009;Guye et al 2006;Drake et al 1998;Sterman 1981) but also for cryptogenic epilepsy (Hongou et al 1993;Sakkalis et al 2008). Initially, we had expected to observe the strongest epilepsy related effects in the delta-theta but not in the alpha band, because some but not all of these studies observed predominantly elevated delta-theta band activity.…”

“…Greater low frequency activity in children compared to adults or in younger compared to older children is a robust EEG marker of normal developmental immaturity (i.e., the opposite of normal development), and thus a marker of developmental lag within an age group. However, low frequency activity is not only greater in healthy children compared to adults but also in patients suffering from a wide range of chronic functional brain disorders such as Parkinson's disease (Moazami-Goudarzi et al 2008), pain Stern et al 2006;Ray et al 2009;Walton et al 2010), tinnitus (Llinas et al 1999;Moazami-Goudarzi et al 2010), neuropsychiatric disorders , and epilepsy (Sarnthein et al 2003;Clemens et al 2000;Clemens 2004b;Sakkalis et al 2008;Sterman 1981;Drake et al 1998;Miyauchi et al 1991;Gibbs et al 1943). Epilepsy is one of the most common neurological diseases with typical abnormal patterns in the EEG or MEG (magnetoencephalogram).…”

Characteristics of the Cortical and Hippocampal EEG Power Spectra of Rabbits during Normal Behavioral States and after Administration of CNS Acting Drugs

“…Recently, power spectral analysis is often employed in experimental as well as clinical studies in order to qualitatively and quanti tatively clarify the EEG changes caused by CNS acting drugs (1-5) or psychological stress (6) and to diagnose cerebral disorders (7)(8)(9)(10). It might be possible to characterize the alterations of the CNS caused by drugs or other agents as alterations of spectra by this analysis, if better techniques were developed.…”

A robust orthogonal algorithm for system identification and time-series analysis

“…For example, the techniques are frequently applied to analyse electroencephalograms (EEG), e.g. in epileptics (Sterman 1981). The FFT is Often used to obtain power spectra of respiration and heart rate, e.g.…”