Mortality Risk Assessment Using Deep Learning-Based Frequency Analysis of EEG and EOG in Sleep.

Study Objectives: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Methods: Power spectra from PSGs of 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is caused by motor disinhibition during REM sleep and is a strong early predictor of Parkinson's disease. However, screening questionnaires for iRBD lack specificity due to other sleep disorders that mimic the symptoms. Nocturnal wrist actigraphy has shown promise in detecting iRBD by measuring sleep-related motor activity, but it relies on sleep diary-defined sleep periods, which are not always available.

SViT: A Spectral Vision Transformer for the Detection of REM Sleep Behavior Disorder.

REM sleep behavior disorder (RBD) is a parasomnia with dream enactment and presence of REM sleep without atonia (RSWA). RBD diagnosed manually via polysomnography (PSG) scoring, which is time intensive. Isolated RBD (iRBD) is also associated with a high probability of conversion to Parkinson's disease.

Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire.

Background: Isolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used.

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive, subject to interscorer variability, and requires high level of expertise. Here, we present an automatic and interpretable diagnostic tool for RBD that analyzes PSGs using end-to-end deep neural networks.

Age estimation from sleep studies using deep learning predicts life expectancy.

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90.

The genetic etiology of periodic limb movement in sleep.

Study Objectives: Periodic limb movement in sleep is a common sleep phenotype characterized by repetitive leg movements that occur during or before sleep. We conducted a genome-wide association study (GWAS) of periodic limb movements in sleep (PLMS) using a joint analysis (i.e.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

Background: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.

Arousal characteristics in patients with Parkinson's disease and isolated rapid eye movement sleep behavior disorder.

Study Objectives: Patients diagnosed with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) and Parkinson's disease (PD) have altered sleep stability reflecting neurodegeneration in brainstem structures. We hypothesize that neurodegeneration alters the expression of cortical arousals in sleep.

Methods: We analyzed polysomnography data recorded from 88 healthy controls (HC), 22 iRBD patients, 82 de novo PD patients without RBD, and 32 with RBD (PD + RBD).

Cortical arousal frequency is increased in narcolepsy type 1.

Study Objectives: Hypocretin deficient narcolepsy (type 1, NT1) presents with multiple sleep abnormalities including sleep-onset rapid eye movement (REM) periods (SOREMPs) and sleep fragmentation. We hypothesized that cortical arousals, as scored by an automatic detector, are elevated in NT1 and narcolepsy type 2 (NT2) patients as compared to control subjects.

Methods: We analyzed nocturnal polysomnography (PSG) recordings from 25 NT1 patients, 20 NT2 patients, 18 clinical control subjects (CC, suspected central hypersomnia but with normal cerebrospinal (CSF) fluid hypocretin-1 (hcrt-1) levels and normal results on the multiple sleep latency test), and 37 healthy control (HC) subjects.

Predicting Age with Deep Neural Networks from Polysomnograms.

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects.

Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.

Objective: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.

Methods: A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals.