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)

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).

The effect of arousals during sleep onset on estimates of sleep onset latency

It is well established that insomniacs overestimate sleep-onset latency. Furthermore, there is evidence that brief arousals from sleep may occur more frequently in insomnia. This study examined the hypothesis that brief arousals from sleep influence the perception of sleep-onset latency. An average of four sleep onsets was obtained from each of 20 normal subjects on each of two nonconsecutive, counterbalanced, experimental nights. The experimental nights consisted of a control night (control condition) and a condition in which a moderate respiratory load was applied to increase the frequency of microarousals during sleep onset (mask condition). Subjective estimation of sleep-onset latency and indices of sleep quality were assessed by self-report inventory. Objective measures of sleep-onset latency and microarousals were assessed using polysomnography. Results showed that sleep-onset latency estimates were longer in the mask condition than in the control condition, an effect not reflected in objective sleep-stage scoring of sleep-onset latency. Furthermore, an increase in the frequency of brief arousals from sleep was detected in the mask condition, and this is a possible source for the sleep-onset latency increase perceived by the subjects. Findings are consistent with the concept of a physiological basis for sleep misperception in insomnia.

Detecting insomnia: comparison of four self-report measures of sleep in a young adult population

The sensitivity and specificity of four self-report measures of disordered sleep - the Sleep Impairment Index (SII), the Sleep Disorders Questionnaire (SDQ), the Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS) and the Sleep-Wake Activity Inventory (SWAI) - were compared in subjects with insomnia and normal sleep. Nineteen young adult subjects met DSM-IV criteria for primary insomnia and another 19 were normal control subjects. Discriminatory characteristics of each measure were assessed using receiver operator characteristic curve analyses. Discriminatory power was maximised for each measure to produce cut-scores applicable for identification of individuals with insomnia. The DBAS, SII and SDQ psychiatric DIMS subscale were found to correlate, and discriminated well between the two groups. The SWAI nocturnal sleep subscale was not found to be an accurate discriminator. The results suggest differences in the measures in their ability to detect insomnia, and offer guidelines as to the optimal use of test scores to identify young adults suspected of insomnia.

Autonomic activity during human sleep as a function of time and sleep stage

While there is a developing understanding of the influence of sleep on cardiovascular autonomic activity in humans, there remain unresolved issues. In particular, the effect of time within the sleep period, independent of sleep stage, has not been investigated. Further, the influence of sleep on central sympathetic nervous system (SNS) activity is uncertain because results using the major method applicable to humans, the low frequency (LF) component of heart rate Variability (HRV), have been contradictory, and because the method itself is open to criticism. Sleep and cardiac activity were measured in 14 young healthy subjects on three nights. Data was analysed in 2-min epochs. All epochs meeting specified criteria were identified, beginning 2 h before, until 7 h after, sleep onset. Epoch values were allocated to 30-min bins and during sleep were also classified into stage 2, slow wave sleep (SWS) and rapid eye movement (REM) sleep. The measures of cardiac activity were heart irate (HR), blood pressure (BP), high frequency (HF) and LF components of HRV and pre-ejection period (PEP). During non-rapid eye movement (NREM) sleep autonomic balance shifted from sympathetic to parasympathetic dominance, although this appeared to be more because of a shift in parasympathetic nervous system (PNS) activity. Autonomic balance during REM was in general similar to wakefulness. For BP and the HF and LF components the change occurred abruptly at sleep onset and was then constant over time within each stage of sleep, indicating that any change in autonomic balance over the sleep period is a consequence of the changing distribution of sleep stages. Two variables, HR and PEP, did show time effects reflecting a circadian influence over HR and perhaps time asleep affecting PEP. While both the LF component and PEP showed changes consistent with reduced sympathetic tone during sleep, their pattern of change over time differed.

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.