Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

Objective.Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.Approach.We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.Main results.For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.Significance.The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.

Studying sleep: towards the identification of hypnogram features that drive expert interpretation.

STUDY OBJECTIVES: Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation of the hypnogram is a difficult task and requires domain knowledge and "clinical intuition." This study aimed to uncover which features of the hypnogram drive interpretation by physicians. In other words, make explicit which features physicians implicitly look for in hypnograms. METHODS: Three sleep experts evaluated up to 612 hypnograms, indicating normal or abnormal sleep structure and suspicion of disorders. ElasticNet and convolutional neural network classification models were trained to predict the collected expert evaluations using hypnogram features and stages as input. The models were evaluated using several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, and confusion matrices. Finally, model coefficients and visual analytics techniques were used to interpret the models to associate hypnogram features with expert evaluation. RESULTS: Agreement between models and experts (Kappa between 0.47 and 0.52) is similar to agreement between experts (Kappa between 0.38 and 0.50). Sleep fragmentation, measured by transitions between sleep stages per hour, and sleep stage distribution were identified as important predictors for expert interpretation. CONCLUSIONS: By comparing hypnograms not solely on an epoch-by-epoch basis, but also on these more specific features that are relevant for the evaluation of experts, performance assessment of (automatic) sleep-staging and surrogate sleep trackers may be improved. In particular, sleep fragmentation is a feature that deserves more attention as it is often not included in the PSG report, and existing (wearable) sleep trackers have shown relatively poor performance in this aspect.

Temporal dynamics of awakenings from slow-wave sleep in non-rapid eye movement parasomnia.

Non-rapid eye movement parasomnia disorders, also called disorders of arousal, are characterized by abnormal nocturnal behaviours, such as confusional arousals or sleep walking. Their pathophysiology is not yet fully understood, and objective diagnostic criteria are lacking. It is known, however, that behavioural episodes occur mostly in the beginning of the night, after an increase in slow-wave activity during slow-wave sleep. A better understanding of the prospect of such episodes may lead to new insights in the underlying mechanisms and eventually facilitate objective diagnosis. We investigated temporal dynamics of transitions from slow-wave sleep of 52 patients and 79 controls. Within the patient group, behavioural and non-behavioural N3 awakenings were distinguished. Patients showed a higher probability to wake up after an N3 bout ended than controls, and this probability increased with N3 bout duration. Bouts longer than 15 min resulted in an awakening in 73% and 34% of the time in patients and controls, respectively. Behavioural episodes reduced over sleep cycles due to a reduction in N3 sleep and a reducing ratio between behavioural and non-behavioural awakenings. In the first two cycles, N3 bouts prior to non-behavioural awakenings were significantly shorter than N3 bouts advancing behavioural awakenings in patients, and N3 awakenings in controls. Our findings provide insights in the timing and prospect of both behavioural and non-behavioural awakenings from N3, which may result in prediction and potentially prevention of behavioural episodes. This work, moreover, leads to a more complete characterization of a prototypical hypnogram of parasomnias, which could facilitate diagnosis.

A computationally efficient algorithm for wearable sleep staging in clinical populations.

This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically "discover" a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics.

Prescription Drugs Used in Insomnia.

In insomnia, the subjective aspects of the sleep complaint are paramount in the diagnostic criteria. Epidemiologic studies increasingly point to a link between insomnia and somatic morbidity and mortality, but until now, only in the subgroup of objectively poor sleepers. Although pharmacologic treatment might offer some benefits to this subgroup of insomnia patients, to date, there is no evidence that hypnotics can ameliorate their health risks. Further unraveling of the neurobiology and genetics of sleep regulation and the pathophysiology of insomnia will help the development of drugs that not only improve subjective sleep complaints but also objective health outcomes.

Prescription Drugs Used in Insomnia.

The scope of this article is to review the effects on sleep of prescription drugs that are commonly prescribed for chronic insomnia in adults. The following groups are discussed: benzodiazepines and its receptor agonists, the dual orexin receptor antagonist suvorexant, melatonin and its receptor agonists, sedating antidepressants, and antipsychotics. Together with the neurobiologic and pharmacologic properties of these drugs, clinical effects are described, including subjective and objective effects on sleep duration, continuity, and architecture. Medical prescription information is given when available. Recently published American and European guidelines for the treatment of insomnia serve as reference frame.

Prescription Drugs Used in Insomnia.

The scope of this article is to review the effects on sleep of prescription drugs that are commonly prescribed for chronic insomnia in adults. The following groups are discussed: benzodiazepines and its receptor agonists, the dual orexin receptor antagonist suvorexant, melatonin and its receptor agonists, sedating antidepressants, and antipsychotics. Together with the neurobiologic and pharmacologic properties of these drugs, clinical effects are described, including subjective and objective effects on sleep duration, continuity, and architecture. Medical prescription information is given when available. Recently published American and European guidelines for the treatment of insomnia serve as reference frame.

Clonidine administration during intraoperative monitoring for pediatric scoliosis surgery: Effects on central and peripheral motor responses.

OBJECTIVE: To study the effect of clonidine administrated as a co-analgesic during scoliosis surgery, on the neuromonitoring of spinal motor pathways. METHODS: Using standardized intraoperative monitoring, we compared the time course of peripherally and transcranially electrically evoked motor potentials (TcEMEPs) before and after injection of a single bolus of clonidine in children under total intravenous anesthesia (TIVA). MEP data were obtained from 9 patients and somatosensory evoked potentials (SSEPs) were obtained from 2 patients. The potential effect of clonidine on mean blood pressure (BP) was controlled. RESULTS: TcEMEPs from upper and lower limbs rapidly showed significant drops in amplitude after the injection of clonidine. Amplitudes reached minimal values within five minutes and remained very weak for at least 10-20minutes during which monitoring of the central motor pathways was severely compromised. SSEPs were not altered during maximal amplitude depression of the TcEMEPS. CONCLUSIONS: This is the first report showing that clonidine severely interferes with neuromonitoring of the spinal cord motor pathways. The results are discussed in light of the literature describing the effects of dexmedetomidine, another α-2 adrenergic agonist. The experimental and literature data point to central mechanisms taking place at both the spinal and cerebral levels. Therefore, clonidine as well as other α-2 adrenergic agonists should be used with extreme caution in patients for whom neuromonitoring of the motor pathways is required during surgery.