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)

Scoring criteria for portable monitor recordings: a comparison of four hypopnoea definitions in a population-based cohort.

RATIONALE: Limited-channel portable monitors (PMs) are increasingly used as an alternative to polysomnography (PSG) for the diagnosis of obstructive sleep apnoea (OSA). However, recommendations for the scoring of PM recordings are still lacking. Pulse-wave amplitude (PWA) drops, considered as surrogates for EEG arousals, may increase the detection sensitivity for respiratory events in PM recordings. OBJECTIVES: To investigate the performance of four different hypopnoea scoring criteria, using 3% or 4% oxygen desaturation levels, including or not PWA drops as surrogates for EEG arousals, and to determine the impact of measured versus reported sleep time on OSA diagnosis. METHODS: Subjects drawn from a population-based cohort underwent a complete home PSG. The PSG recordings were scored using the 2012 American Academy of Sleep Medicine criteria to determine the apnoea-hypopnoea index (AHI). Recordings were then rescored using only parameters available on type 3 PM devices according to different hypopnoea criteria and patients-reported sleep duration to determine the 'portable monitor AHIs' (PM-AHIs). MAIN RESULTS: 312 subjects were included. Overall, PM-AHIs showed a good concordance with the PSG-based AHI although it tended to slightly underestimate it. The PM-AHI using 3% desaturation without PWA drops showed the best diagnostic accuracy for AHI thresholds of ≥5/h and ≥15/h (correctly classifying 94.55% and 93.27% of subjects, respectively, vs 80.13% and 87.50% with PWA drops). There was a significant but modest correlation between PWA drops and EEG arousals (r=0.20, p=0.0004). CONCLUSION: Interpretation of PM recordings using hypopnoea criteria which include 3% desaturation without PWA drops as EEG arousal surrogate showed the best diagnosis accuracy compared with full PSG.

Treatment of snoring and sleep apnea syndrome with a removable mandibular advancement device in patients without TMD

INTRODUCTION: Among the sleep disorders reported by the American Academy of Sleep, the most common is obstructive sleep apnea-hypopnea syndrome (OSAHS), which is caused by difficulties in air passage and complete interruption of air flow in the airway. This syndrome is associated with increased morbidity and mortality in apneic individuals. OBJECTIVE: It was the objective of this paper to evaluate a removable mandibular advancement device as it provides a noninvasive, straightforward treatment readily accepted by patients. METHODS: In this study, 15 patients without temporomandibular disorders (TMD) and with excessive daytime sleepiness or snoring were evaluated. Data were collected by means of: Polysomnography before and after placement of an intraoral appliance, analysis of TMD signs and symptoms using a patient history questionnaire, muscle and TMJ palpation. RESULTS: After treatment, the statistical analysis (t-test, and the before and after test) showed a mean reduction of 77.6% (p=0.001) in the apnea-hypopnea index, an increase in lowest oxyhemoglobin saturation (p=0.05), decrease in desaturation (p=0.05), decrease in micro-awakenings or EEG arousals (p=0.05) and highly significant improvement in daytime sleepiness (p=0.005), measured by the Epworth Sleepiness Scale. No TMD appeared during the monitoring period. CONCLUSION: The oral device developed in this study was considered effective for mild to moderate OSAHS.

Treatment of snoring and sleep apnea syndrome with a removable mandibular advancement device in patients without TMD

INTRODUCTION: Among the sleep disorders reported by the American Academy of Sleep, the most common is obstructive sleep apnea-hypopnea syndrome (OSAHS), which is caused by difficulties in air passage and complete interruption of air flow in the airway. This syndrome is associated with increased morbidity and mortality in apneic individuals. OBJECTIVE: It was the objective of this paper to evaluate a removable mandibular advancement device as it provides a noninvasive, straightforward treatment readily accepted by patients. METHODS: In this study, 15 patients without temporomandibular disorders (TMD) and with excessive daytime sleepiness or snoring were evaluated. Data were collected by means of: Polysomnography before and after placement of an intraoral appliance, analysis of TMD signs and symptoms using a patient history questionnaire, muscle and TMJ palpation. RESULTS: After treatment, the statistical analysis (t-test, and the "before and after" test) showed a mean reduction of 77.6% (p=0.001) in the apnea-hypopnea index, an increase in lowest oxyhemoglobin saturation (p=0.05), decrease in desaturation (p=0.05), decrease in micro-awakenings or EEG arousals (p=0.05) and highly significant improvement in daytime sleepiness (p=0.005), measured by the Epworth Sleepiness Scale. No TMD appeared during the monitoring period. CONCLUSION: The oral device developed in this study was considered effective for mild to moderate OSAHS.

Valeur diagnostique des micro-éveils autonomiques comme substitut aux éveils cérébraux dans l'apnée du sommeil

Les troubles respiratoires du sommeil ont une prévalence élevée dans la population générale, l’apnée obstructive du sommeil étant le plus important de ces troubles. Malgré tout, une grande proportion des patients avec apnée ne sont pas diagnostiqués. La méthode la plus complète pour diagnostiquer l’apnée est l’enregistrement d’une nuit de sommeil par polysomnographie, aussi appelée enregistrement de type 1, qui est un processus long et coûteux. Pour surmonter ces difficultés, des appareils d’enregistrements portables (ou de type 3) ont été développés. Toutefois, ces enregistrements de type 3 ne capturent pas l’activité cérébrale, mesurée avec l’électroencéphalographie (EEG). Le présent mémoire décrit une étude comparative entre les enregistrements de type 1 et de type 3. Tous les événements respiratoires d’apnée, d’hypopnée et d’éveils liés à un effort respiratoire (RERA, en anglais) seront analysés ainsi que les éveils cérébraux (ou éveils EEG) et les éveils autonomiques. Ces éveils autonomiques se définissent par une hausse de la fréquence cardiaque suite à un événement respiratoire. Pour enrichir les analyses, les variables respiratoires suivantes ont été étudiées : une chute de la saturation en oxygène de 4 % (ODI), l’index d’apnée-hypopnée (IAH), l’indice de perturbations respiratoires avec apnées + hypopnées + RERAs et les éveils EEG (RDIe, en anglais) et le RDI incluant les éveils autonomiques définis par une augmentation de la fréquence cardiaque de 5 bpm (RDIa5). L’objectif de la présente étude est d’évaluer la proportion d’événements respiratoires avec éveils autonomiques ainsi que leur impact sur le RDI des enregistrements de type 1 et 3. L’hypothèse suggère que les événements avec éveils autonomiques auraient un plus grand impact sur le RDI des enregistrements de type 3 contrairement au type 1. Cette étude inclut 72 sujets ayant suivi un enregistrement de polysomnographie complète de type 1 ainsi que 79 sujets différents ayant suivi un enregistrement ambulatoire de type 3. Les analyses suivantes ont été effectuées : 1) les pourcentages d’événements associés avec seulement des éveils autonomiques dans les enregistrements de type 1 et de type 3 ; 2) les fréquences de migration entre les catégories basses et élevées de sévérité de l’AHI en prenant en compte les événements associés avec seulement des éveils autonomiques ; 3) les Bland-Altman (B-A) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3) et ; 4) les corrélations de Pearson et les coefficients de corrélation intraclasse (ICC) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3). L’utilisation du critère de RDIa5 permet d’ajouter 49 % d’événements comptés avec l’AHI pour les enregistrements de type 1, et 51 % d’événements pour ceux de type 3. La présente étude montre que les éveils autonomiques ont un impact similaire autant pour le RDI des enregistrements de type 3 que de type 1. En conclusion, on peut affirmer que le RDIa5 est acceptable et fiable pour les enregistrements de type 3.

Valeur diagnostique des micro-éveils autonomiques comme substitut aux éveils cérébraux dans l'apnée du sommeil

Les troubles respiratoires du sommeil ont une prévalence élevée dans la population générale, l’apnée obstructive du sommeil étant le plus important de ces troubles. Malgré tout, une grande proportion des patients avec apnée ne sont pas diagnostiqués. La méthode la plus complète pour diagnostiquer l’apnée est l’enregistrement d’une nuit de sommeil par polysomnographie, aussi appelée enregistrement de type 1, qui est un processus long et coûteux. Pour surmonter ces difficultés, des appareils d’enregistrements portables (ou de type 3) ont été développés. Toutefois, ces enregistrements de type 3 ne capturent pas l’activité cérébrale, mesurée avec l’électroencéphalographie (EEG). Le présent mémoire décrit une étude comparative entre les enregistrements de type 1 et de type 3. Tous les événements respiratoires d’apnée, d’hypopnée et d’éveils liés à un effort respiratoire (RERA, en anglais) seront analysés ainsi que les éveils cérébraux (ou éveils EEG) et les éveils autonomiques. Ces éveils autonomiques se définissent par une hausse de la fréquence cardiaque suite à un événement respiratoire. Pour enrichir les analyses, les variables respiratoires suivantes ont été étudiées : une chute de la saturation en oxygène de 4 % (ODI), l’index d’apnée-hypopnée (IAH), l’indice de perturbations respiratoires avec apnées + hypopnées + RERAs et les éveils EEG (RDIe, en anglais) et le RDI incluant les éveils autonomiques définis par une augmentation de la fréquence cardiaque de 5 bpm (RDIa5). L’objectif de la présente étude est d’évaluer la proportion d’événements respiratoires avec éveils autonomiques ainsi que leur impact sur le RDI des enregistrements de type 1 et 3. L’hypothèse suggère que les événements avec éveils autonomiques auraient un plus grand impact sur le RDI des enregistrements de type 3 contrairement au type 1. Cette étude inclut 72 sujets ayant suivi un enregistrement de polysomnographie complète de type 1 ainsi que 79 sujets différents ayant suivi un enregistrement ambulatoire de type 3. Les analyses suivantes ont été effectuées : 1) les pourcentages d’événements associés avec seulement des éveils autonomiques dans les enregistrements de type 1 et de type 3 ; 2) les fréquences de migration entre les catégories basses et élevées de sévérité de l’AHI en prenant en compte les événements associés avec seulement des éveils autonomiques ; 3) les Bland-Altman (B-A) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3) et ; 4) les corrélations de Pearson et les coefficients de corrélation intraclasse (ICC) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3). L’utilisation du critère de RDIa5 permet d’ajouter 49 % d’événements comptés avec l’AHI pour les enregistrements de type 1, et 51 % d’événements pour ceux de type 3. La présente étude montre que les éveils autonomiques ont un impact similaire autant pour le RDI des enregistrements de type 3 que de type 1. En conclusion, on peut affirmer que le RDIa5 est acceptable et fiable pour les enregistrements de type 3.

Sleep respiratory disturbances and arousals at moderate altitude have overlapping electroencephalogram spectral signatures

An ascent to altitude has been shown to result in more central apneas and a shift towards lighter sleep in healthy individuals. This study employs spectral analysis to investigate the impact of respiratory disturbances (central/obstructive apnea and hypopnea or periodic breathing) at moderate altitude on the sleep electroencephalogram (EEG) and to compare EEG changes resulting from respiratory disturbances and arousals. Data were collected from 51 healthy male subjects who spent 1 night at moderate altitude (2590 m). Power density spectra of Stage 2 sleep were calculated in a subset (20) of these participants with sufficient artefact-free data for (a) epochs with respiratory events without an accompanying arousal, (b) epochs containing an arousal and (c) epochs of undisturbed Stage 2 sleep containing neither arousal nor respiratory events. Both arousals and respiratory disturbances resulted in reduced power in the delta, theta and spindle frequency range and increased beta power compared to undisturbed sleep. The similarity of the EEG changes resulting from altitude-induced respiratory disturbances and arousals indicates that central apneas are associated with micro-arousals, not apparent by visual inspection of the EEG. Our findings may have implications for sleep in patients and mountain tourists with central apneas and suggest that respiratory disturbances not accompanied by an arousal may, none the less, impact sleep quality and impair recuperative processes associated with sleep more than previously believed.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

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.