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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. Diagnosis of iRBD is largely based on clinical evaluation and subjective PSG ratings of REM sleep without atonia. Here we show the first application of a novel spectral vision transformer (SViT) to PSG signals for detection of RBD and compare the results to the more conventional convolutional neural network architecture. The vision-based deep learning models were applied to scalograms (30 or 300 s windows) of the PSG data (EEG, EMG and EOG) and the predictions interpreted. A total of 153 RBD (96 iRBD and 57 RBD with PD) and 190 controls were included in the study and 5-fold bagged ensemble was used. Model outputs were analyzed per-patient (averaged), with regards to sleep stage, and the SViT was interpreted using integrated gradients. Models had a similar per-epoch test F1 score. However, the vision transformer had the best per-patient performance, with an F1 score 0.87. Training the SViT on channel subsets, it achieved an F1 score of 0.93 on a combination of EEG and EOG. EMG is thought to have the highest diagnostic yield, but interpretation of our model showed that high relevance was placed on EEG and EOG, indicating these channels could be included for diagnosing RBD.

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. Our aim was to precisely detect iRBD using actigraphy alone by combining two actigraphy-based markers of iRBD - abnormal nighttime activity and 24-hour rhythm disruption. In a sample of 42 iRBD patients and 42 controls (21 clinical controls with other sleep disorders and 21 community controls) from the Stanford Sleep Clinic, the nighttime actigraphy model was optimized using automated detection of sleep periods. Using a subset of 38 iRBD patients with daytime data and 110 age-, sex-, and body-mass-index-matched controls from the UK Biobank, the 24-hour rhythm actigraphy model was optimized. Both nighttime and 24-hour rhythm features were found to distinguish iRBD from controls. To improve the accuracy of iRBD detection, we fused the nighttime and 24-hour rhythm disruption classifiers using logistic regression, which achieved a sensitivity of 78.9%, a specificity of 96.4%, and an AUC of 0.954. This study preliminarily validates a fully automated method for detecting iRBD using actigraphy in a general population.Clinical relevance- Actigraphy-based iRBD detection has potential for large-scale screening of iRBD in the general population.

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., Objective: The aim was to develop a machine learning classifier using high-frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision., Methods: The method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls., Results: The actigraphy classifier achieved 95.2% (95% confidence interval [CI]: 88.3-98.7) sensitivity and 90.9% (95% CI: 82.1-95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7-100.0) with 88.1% sensitivity (95% CI: 79.2-94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms., Conclusions: Actigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large-scale screening of iRBD in the general population. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

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. We optimized hierarchical attention networks in a 5-fold cross validation directly to classify RBD from PSG data recorded in 143 participants with RBD and 147 age-and sex-matched controls. An ensemble model using logistic regression was implemented to fuse decisions from networks trained in various signal combinations. We interpreted the networks using gradient SHAP that attribute relevance of input signals to model decisions. The ensemble model achieved a sensitivity of 91.4 % and a specificity of 86.3 %. Interpretation showed that electroencephalography (EEG) and leg electromyography (EMG) exhibited most patterns with high relevance. This study validates a robust diagnostic tool for RBD and proposes an interpretable and fully automatic framework for end-to-end modeling of other sleep disorders from PSG data. Clinical relevance- This study presents a novel diagnostic tool for RBD that considers neurophysiologic biomarkers in multiple modalities.

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). These patients were also investigated at a 2-year follow-up. Arousals were analyzed using a previously validated automatic system, which used a central electroencephalography lead, electrooculography, and chin electromyography. Multiple linear regression models were fitted to compare group differences at baseline and change to follow-up for arousal index (ArI), shifts in electroencephalographic signals associated with arousals, and arousal chin muscle tone. The regression models were adjusted for known covariates affecting the nature of arousal., Results: In comparison to HC, patients with iRBD and PD + RBD showed increased ArI during REM sleep and their arousals showed a significantly lower shift in α-band power at arousals and a higher muscle tone during arousals. In comparison to HC, the PD patients were characterized by a decreased ArI in non-REM (NREM) sleep at baseline. ArI during NREM sleep decreased further at the 2-year follow-up, although not significantly., Conclusions: Patients with PD and iRBD present with abnormal arousal characteristics as scored by an automated method. These abnormalities are likely to be caused by neurodegeneration of the reticular activation system due to alpha-synuclein aggregation., (© Sleep Research Society 2021. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)