Georg Dorffner

An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 × 7™) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report™). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20–95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen’s kappa: 0.72) between the Somnolyzer 24 × 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen’s kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 × 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen’s kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 × 7 and demonstrated its applicability in clinical routine and sleep studies.

Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard

Interrater variability of sleep stage scorings has an essential impact not only on the reading of polysomnographic sleep studies (PSGs) for clinical trials but also on the evaluation of patients’ sleep. With the introduction of a new standard for sleep stage scorings (AASM standard) there is a need for studies on interrater reliability (IRR). The SIESTA database resulting from an EU‐funded project provides a large number of studies (nu2003=u200372; 56 healthy controls and 16 subjects with different sleep disorders, mean ageu2003±u2003SD: 57.7u2003±u200318.7, 34 females) for which scorings according to both standards (AASM and R&K) were done. Differences in IRR were analysed at two levels: (1) based on quantitative sleep parameter by means of intraclass correlations; and (2) based on an epoch‐by‐epoch comparison by means of Cohen’s kappa and Fleiss’ kappa. The overall agreement was for the AASM standard 82.0% (Cohen’s kappau2003=u20030.76) and for the R&K standard 80.6% (Cohen’s kappau2003=u20030.68). Agreements increased from R&K to AASM for all sleep stages, except N2. The results of this study underline that the modification of the scoring rules improve IRR as a result of the integration of occipital, central and frontal leads on the one hand, but decline IRR on the other hand specifically for N2, due to the new rule that cortical arousals with or without concurrent increase in submental electromyogram are critical events for the end of N2.

Orexin Receptor Antagonism, a New Sleep-Enabling Paradigm: A Proof-of-Concept Clinical Trial

The orexin system is a key regulator of sleep and wakefulness. In a multicenter, double‐blind, randomized, placebo‐controlled, two‐way crossover study, 161 primary insomnia patients received either the dual orexin receptor antagonist almorexant, at 400, 200, 100, or 50 mg in consecutive stages, or placebo on treatment nights at 1‐week intervals. The primary end point was sleep efficiency (SE) measured by polysomnography; secondary end points were objective latency to persistent sleep (LPS), wake after sleep onset (WASO), safety, and tolerability. Dose‐dependent almorexant effects were observed on SE, LPS, and WASO. SE improved significantly after almorexant 400 mg vs. placebo (mean treatment effect 14.4%; P < 0.001). LPS (–18 min (P = 0.02)) and WASO (–54 min (P < 0.001)) decreased significantly at 400 mg vs. placebo. Adverse‐event incidence was dose‐related. Almorexant consistently and dose‐dependently improved sleep variables. The orexin system may offer a new treatment approach for primary insomnia.

Orexin receptor antagonism: an ascending multiple-dose study with almorexant

The objectives of this study were to investigate the multiple-dose tolerability, safety, pharmacokinetics, and pharmacodynamics of the dual orexin receptor antagonist almorexant. Healthy subjects received daily doses of almorexant (100, 200, 400 or 1000 mg) or placebo in the morning for four days followed by two days with evening administration (Days 5–6). Each dose level was investigated in a new group of 10 subjects (eight active, two placebo, 1:1 sex). Dose-dependent increases in frequency and intensity were observed for somnolence and other adverse events. Pharmacokinetics at steady state showed rapid absorption, low concentrations eight hours post-dose, and minimal accumulation. Following evening, administration absorption was delayed and Cmax decreased. Almorexant at 400 and 1000 mg administered in the morning reduced vigilance, alertness, visuomotor coordination, and motor coordination assessed in a psychometric test battery. Polysomnography recordings following evening administration showed a trend towards shorter latency to sleep stages 3 and 4, and shorter latency to, and longer time in, rapid-eye-movement sleep at higher doses when compared to placebo. Whether these findings in healthy subjects translate into relevant sleep-enabling effects in insomnia patients needs to be investigated in future studies.

Computer-assisted sleep classification according to the standard of the American Academy of Sleep Medicine: validation study of the AASM version of the Somnolyzer 24 × 7.

Background: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K). Methods: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21–86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers. Results: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen’s kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76). Conclusion: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum.

Direct Effect of Sunshine on Suicide

IMPORTANCEnIt has been observed that suicidal behavior is influenced by sunshine and follows a seasonal pattern. However, seasons bring about changes in several other meteorological factors and a seasonal rhythm in social behavior may also contribute to fluctuations in suicide rates.nnnOBJECTIVEnTo investigate the effects of sunshine on suicide incidence that are independent of seasonal variation.nnnDESIGN, SETTING, AND PARTICIPANTSnRetrospective analysis of data on all officially confirmed suicides in Austria between January 1, 1970, and May 6, 2010 (nu2009=u200969u2009462). Data on the average duration of sunshine per day (in hours) were calculated from 86 representative meteorological stations. Daily number of suicides and daily duration of sunshine were differentiated to remove variation in sunshine and variation in suicide incidence introduced by season. Thereafter, several models based on Pearson correlation coefficients were calculated.nnnMAIN OUTCOMES AND MEASURESnCorrelation of daily number of suicides and daily duration of sunshine after mathematically removing the effects of season.nnnRESULTSnSunshine hours and number of suicides on every day from January 1, 1970, to May 6, 2010, were highly correlated (ru2009=u20090.4870; Pu2009<u200910-9). After differencing for the effects of season, a mathematical procedure that removes most of the variance from the data, a positive correlation between number of suicides and hours of daily sunshine remained for the day of suicide and up to 10 days prior to suicide (rmaximumu2009=u20090.0370; Pu2009<u200910-5). There was a negative correlation between the number of suicides and daily hours of sunshine for the 14 to 60 days prior to the suicide event (rminimumu2009=u2009-0.0383; Pu2009<u200910-5). These effects were found in the entire sample and in violent suicides.nnnCONCLUSIONS AND RELEVANCEnDuration of daily sunshine was significantly correlated with suicide frequency independent of season, but effect sizes were low. Our data support the hypothesis that sunshine on the day of suicide and up to 10 days prior to suicide may facilitate suicide. More daily sunshine 14 to 60 days previously is associated with low rates of suicide. Our study also suggests that sunshine during this period may protect against suicide.

Visual learning of affordance based cues

This work is about the relevance of Gibsons concept of affordances [1] for visual perception in interactive and autonomous robotic systems In extension to existing functional views on visual feature representations, we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents We investigate how the originally defined representational concept for the perception of affordances – in terms of using either optical flow or heuristically determined 3D features of perceptual entities – should be generalized to using arbitrary visual feature representations In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, using both 3D and 2D information In addition, we emphasize a new framework for cueing and recognition of affordance-like visual entities that could play an important role in future robot control architectures We argue that affordance-like perception should enable systems to react to environment stimuli both more efficient and autonomous, and provide a potential to plan on the basis of responses to more complex perceptual configurations We verify the concept with a concrete implementation applying state-of-the-art visual descriptors and regions of interest that were extracted from a simulated robot scenario and prove that these features were successfully selected for their relevance in predicting opportunities of robot interaction.

EEG-based drivers' drowsiness monitoring using a hierarchical Gaussian mixture model

We developed an EEG-based probabilistic model, which effectively predicts drowsiness levels of thirty-two subjects involved in a moving base driving simulator experiment. A hierarchical Gaussian mixture model (hGMM) with two mixture components at the lower hierarchical level is used. Each mixture models data density distribution of one of the two drowsiness cornerstones/classes represented by 4-second long EEG segments with low and high drowsiness levels. We transfer spectral contents of each EEG segment into a compact form of autoregressive model coefficients. The Karolinska drowsiness scoring method is used to initially label data belonging to individual classes. We demonstrate good agreement between Karolinska drowsiness scores and the predicted drowsiness, when the hGMM is applied to continuously monitor drowsiness over the time-course of driving sessions. The computations associated with the approach are fast enough to build up a practical real-time drowsiness monitoring system.

Learning to perceive affordances in a framework of developmental embodied cognition

Recently, the aspect of visual perception has been explored in the context of Gibsons concept of affordances in various ways. We focus in this work on the importance of developmental learning and the perceptual cueing for an agents anticipation of opportunities for interaction, in extension to functional views on visual feature representations. The concept for the incremental learning of complex from basic affordances is presented in relation to learning of specific affordance features. We demonstrate the learning of causal relations between visual cues and associated anticipated interactions by reinforcement learning of predictive perceptual states. The work pursues a recently presented framework for cueing and recognition of affordance-based visual entities that plays an important role in robot control architectures, in analogy to human perception. We experimentally verify the concept within a real world robot scenario by learning predictive features from delayed rewards, and prove that features were selected for their relevance in predicting opportunities for interaction.

In search of objective components for sleep quality indexing in normal sleep

The main goal of this study was to investigate to what extent polysomnographic (PSG) recordings of nocturnal human sleep can provide information about sleep quality in terms of correlation with a set of daytime measures. These measures were designed with the aim of comprising selected quality of night sleep and consist of subjective sleep quality ratings, neuropsychological tests and physiological parameters. First, a factor analysis model was applied to the large number of daytime measures of sleep quality in order to detect their latent structure. Secondly, in addition to the gold standard sleep staging method to arrive at variables about sleep architecture from PSG, we applied a recently developed continuous sleep representation by considering the probabilistic sleep model (PSM) describing the microstructure of sleep. Significant correlations between sleep architecture and daytime variables of sleep quality were found. Both the factor analysis and the PSM helped maximize the information about this relationship.