A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Detection and description of non-linear interdependence in normal multichannel human EEG data

Objectives: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Methods: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Results: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Conclusions: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Comparison of the distributions of classical and adaptively aligned EEG power spectra

The distributions of eyes-closed resting electroencephalography (EEG) power spectra and their residuals were described and compared using classically averaged and adaptively aligned averaged spectra. Four minutes of eyes-closed resting EEG was available from 69 participants. Spectra were calculated with 0.5-Hz resolution and were analyzed at this level. It was shown that power in the individual 0.5 Hz frequency bins can be considered normally distributed when as few as three or four 2-second epochs of EEG are used in the average. A similar result holds for the residuals. Power at the peak Alpha frequency has quite different statistical behaviour to power at other frequencies and it is considered that power at peak Alpha represents a relatively individuated process that is best measured through aligned averaging. Previous analyses of contrasts in upper and lower alpha bands may be explained in terms of the variability or distribution of the peak Alpha frequency itself.

Left-right information flow in the brain during EEG arousals

In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)

X-linked myoclonic epilepsy with spasticity and intellectual disability - Mutation in the homeobox gene ARX

Objective: To describe a new syndrome of X-linked myoclonic epilepsy with generalized spasticity and intellectual disability (XMESID) and identify the gene defect underlying this disorder. Methods: The authors studied a family in which six boys over two generations had intractable seizures using a validated seizure questionnaire, clinical examination, and EEG studies. Previous records and investigations were obtained. Information on seizure disorders was obtained on 271 members of the extended family. Molecular genetic analysis included linkage studies and mutational analysis using a positional candidate gene approach. Results: All six affected boys had myoclonic seizures and TCS; two had infantile spasms, but only one had hypsarrhythmia. EEG studies show diffuse background slowing with slow generalized spike wave activity. All affected boys had moderate to profound intellectual disability. Hyperreflexia was observed in obligate carrier women. A late-onset progressive spastic ataxia in the matriarch raises the possibility of late clinical manifestations in obligate carriers. The disorder was mapped to Xp11.2-22.2 with a maximum lod score of 1.8. As recently reported, a missense mutation (1058C>T/P353L) was identified within the homeodomain of the novel human Aristaless related homeobox gene (ARX). Conclusions: XMESID is a rare X-linked recessive myoclonic epilepsy with spasticity and intellectual disability in boys. Hyperreflexia is found in carrier women. XMESID is associated with a missense mutation in ARX. This disorder is allelic with X-linked infantile spasms (ISSX; MIM 308350) where polyalanine tract expansions are the commonly observed molecular defect. Mutations of ARX are associated with a wide range of phenotypes; functional studies in the future may lend insights to the neurobiology of myoclonic seizures and infantile spasms.

A medial to lateral shift in premovement cortical activity in hemi-Parkinson's disease: Studies of event-related potentials and whole-scalp magneto encephalography

Cortical activity associated with voluntary movement is shifted from medial to lateral premotor areas in Parkinson's disease. This occurs bilaterally, even for unilateral movements. We have used both EEG and MEG to further investigate medial and lateral premotor activity in patients with hemi-Parkinson's disease, in whom basal ganglia impairment is most pronounced in one hemisphere. The CNV, recorded from 21 scalp positions in a Go/NoGo task, was maximal over central medial regions in control subjects. For hemi-Parkinson's disease subjects, activity was shifted more frontally, reduced in the midline and lateralised towards the side of greatest basal ganglia impairment. With 143 channel whole-scalp magneto encephalography (MEG) we are further examining asymmetries in supplementary motor/premotor cortical activity prior to self-paced voluntary movement. In preliminary results, one hemi-Parkinson's disease patient with predominantly left-side symptoms showed strong medial activity consistent with a dominant source in the left supplementary motor area (SMA). Three patients showed little medial activity, but early bilateral sources within lateral premotor cortex. Results suggest greater involvement of lateral premotor rather than the SMA prior to movement in Parkinson's disease and provide evidence for asymmetric function of the SMA in hemi- Parkinson's disease, with reduced activity on the side of greatest basal ganglia deficit.