U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

Neural networks are becoming more and more popular for the analysis of physiological time-series. The most successful deep learning systems in this domain combine convolutional and recurrent layers to extract useful features to model temporal relations. Unfortunately, these recurrent models are difficult to tune and optimize. In our experience, they often require task-specific modifications, which makes them challenging to use for non-experts. We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data. U-Time is a temporal fully convolutional network based on the U-Net architecture that was originally proposed for image segmentation. U-Time maps sequential inputs of arbitrary length to sequences of class labels on a freely chosen temporal scale. This is done by implicitly classifying every individual time-point of the input signal and aggregating these classifications over fixed intervals to form the final predictions. We evaluated U-Time for sleep stage classification on a large collection of sleep electroencephalography (EEG) datasets. In all cases, we found that U-Time reaches or outperforms current state-of-the-art deep learning models while being much more robust in the training process and without requiring architecture or hyperparameter adaptation across tasks.Comment: To appear in Advances in Neural Information Processing Systems (NeurIPS), 201

Data-Driven Analysis of EEG Reveals Concomitant Superficial Sleep During Deep Sleep in Insomnia Disorder

Study Objectives: The subjective suffering of people with Insomnia Disorder (ID) is insufficiently accounted for by traditional sleep classification, which presumes a strict sequential occurrence of global brain states. Recent studies challenged this presumption by showing concurrent sleep- and wake-type neuronal activity. We hypothesized enhanced co-occurrence of diverging EEG vigilance signatures during sleep in ID. Methods: Electroencephalography (EEG) in 55 cases with ID and 64 controls without sleep complaints was subjected to a Latent Dirichlet Allocation topic model describing each 30 s epoch as a mixture of six vigilance states called Topics (T), ranked from N3-related T1 and T2 to wakefulness-related T6. For each stable epoch we determined topic dominance (the probability of the most likely topic), topic co-occurrence (the probability of the remaining topics), and epoch-to-epoch transition probabilities. Results: In stable epochs where the N1-related T4 was dominant, T4 was more dominant in ID than in controls, and patients showed an almost doubled co-occurrence of T4 during epochs where the N3-related T1 was dominant. Furthermore, patients had a higher probability of switching from T1- to T4-dominated epochs, at the cost of switching to N3-related T2-dominated epochs, and a higher probability of switching from N2-related T3- to wakefulness-related T6-dominated epochs. Conclusion: Even during their deepest sleep, the EEG of people with ID express more N1-related vigilance signatures than good sleepers do. People with ID are moreover more likely to switch from deep to light sleep and from N2 sleep to wakefulness. The findings suggest that hyperarousal never rests in ID

Sleep Spindles as Biomarker for Early Detection of Neurodegenerative Disorders

The present invention relates to the use of sleep spindles as a novel biomarker for early diagnosis of synucleinopathies, in particular Parkinson's disease (PD). The method is based on automatic detection of sleep spindles. The method may be combined with measurements of one or more further biomarkers derived from polysomnographic recordings.</p

The European Insomnia Guideline : An update on the diagnosis and treatment of insomnia 2023

Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential-diagnostic purposes (A). Polysomnography should be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders, etc.), treatment-resistant insomnia (A) and for other indications (B). Cognitive-behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (including patients with comorbidities), either applied in-person or digitally (A). When cognitive-behavioural therapy for insomnia is not sufficiently effective, a pharmacological intervention can be offered (A). Benzodiazepines (A), benzodiazepine receptor agonists (A), daridorexant (A) and low-dose sedating antidepressants (B) can be used for the short-term treatment of insomnia (≤ 4 weeks). Longer-term treatment with these substances may be initiated in some cases, considering advantages and disadvantages (B). Orexin receptor antagonists can be used for periods of up to 3 months or longer in some cases (A). Prolonged-release melatonin can be used for up to 3 months in patients ≥ 55 years (B). Antihistaminergic drugs, antipsychotics, fast-release melatonin, ramelteon and phytotherapeutics are not recommended for insomnia treatment (A). Light therapy and exercise interventions may be useful as adjunct therapies to cognitive-behavioural therapy for insomnia (B).Peer reviewe

A validation of wrist actigraphy against polysomnography in patients with schizophrenia or bipolar disorder

Lone Baandrup,1,2 Poul J&oslash;rgen Jennum3 1Center for Neuropsychiatric Schizophrenia Research (CNSR), 2Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Center Glostrup, Mental Health Services &ndash; Capital Region of Denmark, Glostrup, Denmark; 3Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Center for Healthy Ageing, Faculty of Health Sciences, University of Copenhagen, Rigshospitalet, Glostrup, Denmark Purpose: Sleep disturbances are frequent in patients with schizophrenia or bipolar disorder. Actigraphy has been established as a generally reliable method to examine these disturbances across varying time spans, but the validity against polysomnography (PSG) is not well investigated for this population. We validated wrist-worn actigraphy against PSG in a population of chronic, medicated patients with schizophrenia or bipolar disorder. Patients and methods: From a clinical trial, we derived data from 37 patients with schizophrenia and five patients with bipolar disorder who were examined with one-night PSG and concomitant actigraphy. The following sleep variables were compared between the two methods: total sleep time, sleep efficiency, sleep latency, number of awakenings, and time awake after sleep onset. The degree of consistency between the two methods was evaluated using the intraclass correlation coefficient and Bland&ndash;Altman plots. Subgroup analyses included splitting the analyses according to sex, diagnosis, and duration of wakefulness after sleep onset. PSG was considered the gold standard. Results: The intraclass correlation coefficient was high for total sleep time, moderate for the number of awakenings, and low or zero for the other examined sleep variables. These findings were reproduced in the subgroup analyses that compared men and women, as well as patients with bipolar versus schizophrenia spectrum disorders. When excluding patients with extensive periods of wakefulness after the initial sleep period (wake after sleep onset &gt;100&nbsp;minutes), the reliability of the actigraphy-derived sleep variables markedly improved. Conclusion: Actigraphy reliably measures the total sleep time in this specific patient population. For patients without extensive periods of wakefulness after sleep onset, actigraphy might provide a useful measure of sleep efficiency, sleep latency, and number of awakenings. Keywords: actigraphy, polysomnography, validation, schizophrenia, bipolar disorder&nbsp