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Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia—a common sleep disorder—have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia. [ABSTRACT FROM AUTHOR]

The recording of ECG and heart rate are indispensable parameters of cardiorespiratory polysomnography. Complementing neurophysiological signals this allows an assessment of the autonomous nervous regulation during sleep. An evaluation of the nocturnal ECG is possible in terms of cyclical variations of heart rate and in terms of respiration modulated changes of ECG morphology (amplitude of R wave and T wave). This provides a reliable estimation of sleep disordered breathing. The autonomous nervous system is regulated in a totally different way during slow wave sleep and during REM sleep. An assessment of beat-to-beat variability of heart rate allows deriving sleep stages based on the difference in autonomous nervous system regulation. In addition the transitions from wakefulness to sleep can be tracked to some degree by an analysis of heart rate variability. This paper presents the technical development of the analysis in a historical sequence from 1980 to 2015. Linked to this the increase in physiological and pathophysiological knowledge derived from the technical developments is presented. [ABSTRACT FROM AUTHOR]

Sleep is not just the absence of wakefulness but a regulated process with an important restorative function. Based on electroencephalographic recordings and characteristic patterns and waveforms we can distinguish wakefulness and five sleep stages grouped into light sleep, deep sleep, and rapid-eye-movement (REM) sleep. In order to explore the functions of sleep and sleep stages, we investigated the dynamics of sleep stages over the night and of heart-rate variability during the different sleep stages. Sleep stages and intermediate wake states have different distributions of their duration and this allows us to create a model for the temporal sequence of sleep stages and wake states. Heart rate is easily accessed with a high precision by the recording and analysis of the electrocardiogram (ECG). Heart-rate regulation is part of the autonomous nervous system and sympathetic tone is strongly influenced by the sleep stages. [ABSTRACT FROM AUTHOR]

Presents a study which examined the inter-rater reliability of a computer-based visual sleep scoring system in a number of accredited sleep centers in Germany. Overview of the study population; Methodology; Results and discussion.

The objective of the present study was to quantify repolarization lability and its association with sex, sleep stage, and cardiovascular mortality. We analyzed polysomnographic recordings of 2,263 participants enrolled in the Sleep Heart Health Study (SHHS-2). Beat-to-beat QT interval variability (QTV) was quantified for consecutive epochs of 5 min according to the dominant sleep stage [wakefulness, nonrapid eye movement stage 2 (NREM2), nonrapid eye movement stage 3 (NREM3), and rapid eye movement (REM)]. To explore the effect of sleep stage and apneahypopnea index (AHI) on QT interval parameters, we used a general linear mixed model and mixed ANOVA. The Cox proportional hazards model was used for cardiovascular disease (CVD) death prediction. Sex-related differences in T wave amplitude (P < 0.001) resulted in artificial QTV differences. Hence, we corrected QTV parameters by T wave amplitude for further analysis. Sleep stages showed a significant effect (P < 0.001) on QTV. QTV was decreased in deep sleep compared with wakefulness, was higher in REM than in NREM, and showed a distinct relation to AHI in all sleep stages. The T wave amplitude-corrected QTV index (cQTVi) in REM sleep was predictive of CVD death (hazard ratio: 2.067, 95% confidence interval: 1.105-3.867, P < 0.05) in a proportional hazards model. We demonstrated a significant impact of sleep stages on ventricular repolarization variability. Sex differences in QTV are due to differences in T wave amplitude, which should be corrected for. Independent characteristics of QTV measures to sleep stages and AHI showed different behaviors of heart rate variability and QTV expressed as cQTVi. cQTVi during REM sleep predicts CVD death. [ABSTRACT FROM AUTHOR]

We performed a mathematical analysis of functional connectivity in electroencephalography (EEG) of patients with obstructive sleep apnea (OSA) (N = 10; age: 52.8 ± 13 years; median age: 49 years; male/female ratio: 7/3), compared with a group of apparently healthy participants (N = 15; age: 51.5 ± 29.5 years; median age: 42 years; male/female ratio: 8/7), based on the calculation of wavelet bicoherence from nighttime polysomnograms. Having observed the previously known phenomenon of interhemispheric synchronization deterioration, we demonstrated a compensatory increase in intrahemispheric connectivity, as well as a slight increase in the connectivity of the central and occipital areas for high-frequency EEG activity. Significant changes in functional connectivity were extremely stable in groups of apparently healthy participants and OSA patients, maintaining the overall pattern when comparing different recording nights and various sleep stages. The maximum variability of the connectivity was observed at fast oscillatory processes during REM sleep. The possibility of observing some changes in functional connectivity of brain activity in OSA patients in a state of passive wakefulness opens up prospects for further research. Developing the methods of hypnogram evaluation that are independent of functional connectivity may be useful for implementing a medical decision support system. [ABSTRACT FROM AUTHOR]

We developed a cross-over study design with two interventions in randomized order to compare the effects of sleep fragmentation and partial sleep restriction on cardiac autonomic tone. Twenty male subjects (40.6 ± 7.5 years old) underwent overnight polysomnography during 2 weeks, each week containing one undisturbed baseline night, one intervention night (either sleep restriction with 5 h of sleep or sleep fragmentation with awakening every hour) and two undisturbed recovery nights. Parameters of heart rate variability (HRV) were used to assess cardiac autonomic modulation during the nights. Sleep restriction showed significant higher heart rate (p = 0.018) and lower HRV-pNN50 (p = 0.012) during sleep stage N1 and lower HRV-SDNN (p = 0.009) during wakefulness compared to the respective baseline. For HR and SDNN there were recovery effects. There was no significant difference comparing fragmentation night and its baseline. Comparing both intervention nights, sleep restriction had lower HRV high frequency (HF) components in stage N1 (p = 0.018) and stage N2 (p = 0.012), lower HRV low frequency (LF) (p = 0.007) regarding the entire night and lower SDNN (p = 0.033) during WASO during sleep. Sleep restriction increases sympathetic tone and decreases vagal tone during night causing increased autonomic stress, while fragmented sleep does not affect cardiac autonomic parameters in our sample. [ABSTRACT FROM AUTHOR]

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time–Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses. [ABSTRACT FROM AUTHOR]

Appearances of alpha waves in the sleep electrencephalogram indicate physiological, brief states of awakening that lie in between wakefulness and sleep. These microstates may also cause the loss in sleep quality experienced by individuals suffering from insomnia. To distinguish such pathological awakenings from physiological ones, differences in alpha-wave characteristics between transient awakening and wakefulness observed before the onset of sleep were studied. In polysomnographic datasets of sleep-healthy participants ( n = 18) and patients with insomnia ( n = 10), alpha waves were extracted from the relaxed, wake state before sleep onset, wake after sleep-onset periods and arousals of sleep. In these, alpha frequency and variability were determined as the median and standard deviation of inverse peak-to-peak intervals. Before sleep onset, patients with insomnia showed a decreased alpha variability compared with healthy participants ( P < 0.05). After sleep onset, both groups showed patterns of decreased alpha frequency that was lower for wake after sleep-onset periods of shorter duration. For patients with insomnia, alpha variability increased for short wake after sleep-onset periods. Major differences between the two groups were encountered during arousal. In particular, the alpha frequency in patients with insomnia rebounded to wake levels, while the frequency in healthy participants remained at the reduced level of short wake after sleep-onset periods. Reductions in alpha frequency during wake after sleep-onset periods may be related to the microstate between sleep and wakefulness that was described for such brief awakenings. Reduced alpha variability before sleep may indicate a dysfunction of the alpha generation mechanism in insomnia. Alpha characteristics may also prove valuable in the study of other sleep and attention disorders. [ABSTRACT FROM AUTHOR]

Objective: Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts – a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects’ wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep. Methods: Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths. Results: The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events. Conclusion: During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration. Significance: The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects’ normal sleep environment. [ABSTRACT FROM AUTHOR]

To investigate inter-rater agree- ment between scorers from three centers with clinical (Marburg University, UMA) or research (German Aerospace Center, DLR and Dortmund University, UDO) backgrounds. Additionally, sleep scoring rules of the new AASM manual for the scoring of sleep and associated events were reviewed regarding possible implications for inter-rater agreement. Each of three centers contributed 20 nights. All 60 nights (37 subjects, 9 female, mean age ± sd = 41.8 ± 16.1 years) were scored by each center according to the rules of Rechtschaffen and Kales. Twenty subjects underwent obstructive sleep apnea (OSA) diagnosis, the remaining subjects participated in studies on the effects of traffic noise on sleep and were free of intrinsic sleep disorders. According to kappa statistics, inter-rater agreement between the three centers was excellent in 38 %, fair to good in 62 % and never poor. Mean kappa values decreased in the order rapid eye movement sleep, wake, stage 2, slow wave sleep, stage 4, stage 1 and stage 3. Time spent in the different sleep stages was positively correlated with kappa values. Pairwise comparisons revealed that agreement on stage 1 was significantly worse for UDO, but concerning all other stages none of the centers deviated significantly from the other two. Analyses of Venn diagrams showed tendencies of UDO for scoring wake alone and of UMA for scoring stage 4 alone. Differences between clinical and research centers were overall minor. Pairwise kappa comparisons of several centers/scorers as well as Venn diagrams may detect systematic deviances of single centers/ scorers that consequently should receive additional training. The revised AASM rules for sleep scoring will most likely increase inter-rater agreement, but future studies will have to prove this. [ABSTRACT FROM AUTHOR]

Objective: Portable oximetry has been shown to be a promising candidate for large-scale obstructive sleep apnea screening. In polysomnography (PSG), the gold standard OSA diagnosis test, the oxygen desaturation index (ODI) is usually computed from desaturation events occurring during sleep periods only, i.e. overnight desaturations occurring during or overlapping with a wake state are excluded. However, for unattended home oximetry, all desaturations are taken into account since no reference electroencephalogram is available for sleep staging. We aim to evaluate the hypothesis that the predictive power of oximetry for OSA screening is not impaired when reference sleep stages are not available. Approach: We used a PSG clinical database of 887 individuals from a representative Săo Paulo (Brazil) population sample. Using features derived from the oxygen saturation time series and demographic information, OxyDOSA, a published machine learning model, was trained to distinguish between non-OSA and OSA individuals using the ODI computed while including versus excluding overnight desaturations overlapping with a wake period, thus mimicking portable and PSG oximetry analyses, respectively. Main results: When excluding wake desaturations, the OxyDOSA model had an AUROC = 94.9 ± 1.6, Se = 85.9 ± 2.8, Sp = 90.1 ± 2.6 and F1 = 86.4 ± 2.7. When considering wake desaturations, the OxyDOSA model had an AUROC = 94.4 ± 1.6, Se = 88.0 ± 2.0, Sp = 87.7 ± 2.9 and F1 = 86.2 ± 2.4. Non-inferiority was demonstrated (p = 0.049) at a tolerance level of 3%. In addition, analysis of the desaturations excluded by PSG oximetry analysis suggests that up to 21% of the total number of desaturations might actually be related to apneas or hypopneas. Significance: This analysis of a large representative population sample provided strong evidence that the predictive power of oximetry for OSA screening using the OxyDOSA model is not impaired when reference sleep stages are not available. This finding motivates the usage of portable oximetry for OSA screening. [ABSTRACT FROM AUTHOR]

Objective: To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms. Methods: The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( $d_1$, d_2$ , d_3$, d_4$). The study employed sleep onset periods from 16 control and 16 subjects with insomnia. Results: State-spaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. FIS managed automated sleep scoring robust to intersubjects’ and interraters’ variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated hypnograms. A statistical set of unpaired two-tailed t$ -tests suggested that distances d_1$ , d_2 ) to characterize sleeping patterns. The logistic regression model classified control and subjects with insomnia with sensitivity $87 \%$ , specificity $75 \%$, and accuracy $81 \%$. Conclusion: Our approach can perform a supportive role in either biosignal processing, sleep staging, insomnia characterization, or all the previous, coping with time-consuming procedures and massive data volumes of standard protocols. Significance: The introduction of graph spectral theory and logistic regression for the diagnosis of insomnia represents a novel concept, attempting to describe complex sleep dynamics throughout transitions networks and scalar measures. [ABSTRACT FROM PUBLISHER]

Phase synchronization between two weakly coupled oscillators has been studied in chaotic systems for a long time. However, it is difficult to unambiguously detect such synchronization in experimental data from complex physiological systems. In this paper we review our study of phase synchronization between heartbeat and respiration in 150 healthy subjects during sleep using an automated procedure for screening the synchrograms. We found that this synchronization is significantly enhanced during non-rapid-eye-movement (non-REM) sleep (deep sleep and light sleep) and is reduced during REM sleep. In addition, we show that the respiration signal can be reconstructed from the heartbeat recordings in many subjects. Our reconstruction procedure, which works particularly well during non-REM sleep, allows the detection of cardiorespiratory synchronization even if only heartbeat intervals were recorded. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Tolterodine, a drug for the treatment of overactive bladder symptoms, has a limited entry into the brain, which makes cognitive side effects seldom. However, some case reports have described central-nervous side effects such as sleepiness. The aim of this retrospective analysis was to investigate whether tolterodine-related effects on sleep stage parameters could be explained by different CYP2D6 metabolizer characteristics of subjects. Methods: Data were taken from two randomized, double-blind, placebo-controlled studies conducted in a cross-over design. Forty-eight volunteers underwent 4 two-night attended polysomnographic studies. Subjective quality of sleep and cognitive function were assessed. A single dose of 4mg tolterodine or placebo was administered before sleep. Forty-four volunteers gave informed consent for genotyping. We found 19 extensive metabolizers (EM), 20 intermediate metabolizers (IM), 4 poor metabolizers (PM) and 1 ultrarapid metabolizer. There were no significant differences between the groups regarding demographic data. Results: Rapid eye movement (REM) sleep duration as a percentage of total sleep time showed significant reduction (p =0.019) in the group carrying one or more deficient alleles (IM+PM). No significant difference was found with two active alleles of CYP2D6 in the EM group. REM latencies under tolterodine displayed a tendency towards prolongation, which was irrespective of the metabolizer status. Subjective sleep parameters did not show statistically significant changes after tolterodine. Cognitive skills were not affected. Conclusion: Our retrospective analysis reveals that a decrease of REM sleep under tolterodine is found only in individuals carrying one or two deficient CYP2D6 alleles. [Copyright &y& Elsevier]

Abstract: Objective: The effects of aircraft noise on sleep macrostructure (Rechtschaffen and Kales) and microstructure (American Sleep Disorders Association [ASDA] arousal criteria) were investigated. Patients and methods: For each of 10 subjects (mean age 35.3 years, 5 males), a baseline night without aircraft noise (control), and two nights with exposure to 64 noise events with a maximum sound pressure level (SPL) of either 45 or 65dBA were chosen. Spontaneous and noise-induced alterations during sleep classified as arousals (ARS), changes to lighter sleep stages (CSS), awakenings including changes to sleep stage 1 (AS1), and awakenings (AWR) were analyzed. Results: The number of events per night increased in the order AWR, AS1, CSS, and ARS under control conditions as well as under the two noise conditions. Furthermore, probabilities for sleep disruptions increased with increasing noise level. ARS were observed about fourfold compared to AWR, irrespective of control or noise condition. Conclusions: Under the conditions investigated, different sleep parameters show different sensitivities, but also different specificities for noise-induced sleep disturbances. We conclude that most information on sleep disturbances can be achieved by investigating robust classic parameters like AWR or AS1, although ASDA electroencephalographic (EEG) arousals might add relevant information in situations with low maximum SPLs, chronic sleep deprivation or chronic exposure. [Copyright &y& Elsevier]

Presents a study which assessed the potential of an adaptive algorithm approach for solving existing problems in interpretation variability for sleep-stage recognition and the low acceptance of automatic systems. Theoretical background; Methodology; Results and discussion.

Sleep deprivation is present not only in sleep disorders but also in numerous high demanding jobs and negatively affects cognition, performance and health. We developed a study design to distinguish the effects and need for recovery of two short-term disturbances – intermittent sleep fragmentation and partial sleep restriction. The randomized within-subjects design contained two weeks each with a baseline night, an intervention night of either sleep deprivation (5 h) or sleep fragmentation (light on every hour) and two undisturbed recovery nights. Twenty healthy male participants (mean age: 39.9 ± 7.4 years, mean BMI: 25.5 ± 2.2 kg/m²) underwent polysomnography, a psychomotor vigilance task (PVT), and subjective questions on well-being and sleep efficiency. Percentage-wise, the restriction night had significant less wake times, less light sleep (stage 1), less REM sleep, but more deep sleep (stage 3) than the fragmentation night. The restriction week displayed a significant recovery effect regarding these sleep stages. The sleep fragmentation week presented a significant recovery effect regarding sleep onset times. PVT performance showed only a slight recovery effect after sleep restriction. Subjective sleep quality was reduced after both interventions with a significant recovery effect during restriction week only. Short-term sleep restriction presented as a stronger sleep disturbance than short-term intermittent sleep fragmentation, including a stronger need for recovery. Already a one night sleep deprivation had an effect beyond two recovery days. The PVT was not sensitive enough to reveal significant changes. Next, autonomic parameters as possible biomarkers will be investigated. • Comparison of sleep fragmentation and partial sleep restriction on sleep and recovery. • Already a one night sleep deprivation had an effect beyond two recovery days. • Sleep restriction was perceived as a stronger disturbance than sleep fragmentation. • Sleep restriction had a stronger need for recovery. • PVT was not sensitive enough to reveal significant changes. [ABSTRACT FROM AUTHOR]