Increased muscle activity during sleep and more RBD symptoms in H1N1-(Pandemrix)-vaccinated narcolepsy type 1 patients compared with their non-narcoleptic siblings.

STUDY OBJECTIVES: Narcolepsy type 1 (NT1) is characterized by unstable sleep-wake and muscle tonus regulation during sleep. We characterized dream enactment and muscle activity during sleep in a cohort of post-H1N1 NT1 patients and their siblings, and analyzed whether clinical phenotypic characteristics and major risk factors are associated with increased muscle activity. METHODS: RBD symptoms and polysomnography m. tibialis anterior electromyographical signals [long (0.5-15 s); short (0.1-0.49 s)] were compared between 114 post-H1N1 NT1 patients and 89 non-narcoleptic siblings. Association sub-analyses with RBD symptoms, narcoleptic symptoms, CSF hypocretin-1 levels, and major risk factors [H1N1-(Pandemrix)-vaccination, HLA-DQB1*06:02-positivity] were performed. RESULTS: RBD symptoms, REM and NREM long muscle activity indices and REM short muscle activity index were significantly higher in NT1 patients than siblings (all p < 0.001). Patients with undetectable CSF hypocretin-1 levels (<40 pg/ml) had significantly more NREM periodic long muscle activity than patients with low but detectable levels (40-150 pg/ml) (p = 0.047). In siblings, REM and NREM sleep muscle activity indices were not associated with RBD symptoms, other narcolepsy symptoms, or HLA-DQB1*06:02-positivity. H1N1-(Pandemrix)-vaccination status did not predict muscle activity indices in patients or siblings. CONCLUSION: Increased REM and NREM muscle activity and more RBD symptoms is characteristic of NT1, and muscle activity severity is predicted by hypocretin deficiency severity but not by H1N1-(Pandemrix)-vaccination status. In the patients' non-narcoleptic siblings, neither RBD symptoms, core narcoleptic symptoms, nor the major NT1 risk factors is associated with muscle activity during sleep, hence not indicative of a phenotypic continuum.

Video-polysomnography procedures for diagnosis of rapid eye movement sleep behavior disorder (RBD) and the identification of its prodromal stages: guidelines from the International RBD Study Group.

Video-polysomnography (v-PSG) is essential for diagnosing rapid eye movement (REM) sleep behavior disorder (RBD). Although there are current American Academy of Sleep Medicine standards to diagnose RBD, several aspects need to be addressed to achieve harmonization across sleep centers. Prodromal RBD is a stage in which symptoms and signs of evolving RBD are present, but do not yet meet established diagnostic criteria for RBD. However, the boundary between prodromal and definite RBD is still unclear. As a common effort of the Neurophysiology Working Group of the International RBD Study Group, this manuscript addresses the need for comprehensive and unambiguous v-PSG recommendations to diagnose RBD and identify prodromal RBD. These include: (1) standardized v-PSG technical settings; (2) specific considerations for REM sleep scoring; (3) harmonized methods for scoring REM sleep without atonia; (4) consistent methods to analyze video and audio recorded during v-PSGs and to classify movements and vocalizations; (5) clear v-PSG guidelines to diagnose RBD and identify prodromal RBD. Each section follows a common template: The current recommendations and methods are presented, their limitations are outlined, and new recommendations are described. Finally, future directions are presented. These v-PSG recommendations are intended for both practicing clinicians and researchers. Classification and quantification of motor events, RBD episodes, and vocalizations are however intended for research purposes only. These v-PSG guidelines will allow collection of homogeneous data, providing objective v-PSG measures and making future harmonized multicentric studies and clinical trials possible.

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. Arousals were automatically scored using Multimodal Arousal Detector (MAD), a previously validated automatic wakefulness and arousal detector. Multiple linear regressions were used to compare arousal index (ArI) distributions across groups. Comparisons were corrected for age, sex, body-mass index, medication, apnea-hypopnea index, periodic leg movement index, and comorbid rapid eye movement sleep behavior disorder. RESULTS: NT1 was associated with an average increase in ArI of 4.02 events/h (p = 0.0246) compared to HC and CC, while no difference was found between NT2 and control groups. Additionally, a low CSF hcrt-1 level was predictive of increased ArI in all the CC, NT2, and NT1 groups. CONCLUSIONS: The results further support the hypothesis that a loss of hypocretin neurons causes fragmented sleep, which can be measured as an increased ArI as scored by the MAD.

Nocturnal eye movements in patients with idiopathic rapid eye movement sleep behaviour disorder and patients with Parkinson's disease.

Patients with idiopathic rapid-eye-movement (REM) sleep behaviour disorder (iRBD) have a high risk of converting into manifest α-synucleinopathies. Eye movements (EMs) are controlled by neurons in the lower brainstem, midbrain and frontal areas, and may be affected by the early neurodegenerative process seen in iRBD. Studies have reported impairment of the oculomotor function in patients with Parkinson's disease (PD) during wakefulness, but no studies have investigated EMs during sleep. We aimed to evaluate nocturnal EMs in iRBD and PD, hypothesizing that these patients present abnormal EM distribution during sleep. Twenty-eight patients with periodic limb movement disorder (PLMD), 24 iRBD, 23 PD without RBD (PDwoRBD), 29 PD and RBD (PDwRBD) and 24 controls were included. A validated EM detector automatically identified EM periods between lights off and on. The EM coverage was computed as the percentage of time containing EMs during stable wake after lights off, N1, N2, N3 and REM sleep. Between-group comparisons revealed that PDwRBD had significantly less EM coverage during wake and significantly higher EM coverage during N2 compared to controls and PLMD patients. PDwoRBD showed significantly less EM coverage during wake compared to controls and higher EM coverage during N2 compared to controls and PLMD. Finally, iRBD showed less coverage of EM during wake compared to controls. The same trend was observed between iRBD and controls in N2 but was not significant. The different profiles of EM coverage in iRBD and PD with/without RBD may mirror different stages of central nervous system involvement across neurodegenerative disease progression.

A data-driven system to identify REM sleep behavior disorder and to predict its progression from the prodromal stage in Parkinson's disease.

OBJECTIVES: To investigate electroencephalographic (EEG), electrooculographic (EOG) and micro-sleep abnormalities associated with rapid eye movement (REM) sleep behavior disorder (RBD) and REM behavioral events (RBEs) in Parkinson's disease (PD). METHODS: We developed an automated system using only EEG and EOG signals. First, automatic macro- (30-s epochs) and micro-sleep (5-s mini-epochs) staging was performed. Features describing micro-sleep structure, EEG spectral content, EEG coherence, EEG complexity, and EOG energy were derived. All features were input to an ensemble of random forests, giving as outputs the probabilities of having RBD or not (P (RBD) and P (nonRBD), respectively). A patient was classified as having RBD if P (RBD)≥P (nonRBD). The system was applied to 107 de novo PD patients: 54 had normal REM sleep (PDnonRBD), 26 had RBD (PD + RBD), and 27 had at least two RBEs without meeting electromyographic RBD cut-off (PD + RBE). Sleep diagnoses were made with video-polysomnography (v-PSG). RESULTS: Considering PDnonRBD and PD + RBD patients only, the system identified RBD with accuracy, sensitivity, and specificity over 80%. Among the features, micro-sleep instability had the highest importance for RBD identification. Considering PD + RBE patients, the ones who developed definite RBD after two years had significantly higher values of P (RBD) at baseline compared to the ones who did not. The former were distinguished from the latter with sensitivity and specificity over 75%. CONCLUSIONS: Our method identifies RBD in PD patients using only EEG and EOG signals. Micro-sleep instability could be a biomarker for RBD and for proximity of conversion from RBEs, as prodromal RBD, to definite RBD in PD patients.

External validation of a data-driven algorithm for muscular activity identification during sleep.

Several automated methods for scoring periodic limb movements during sleep (PLMS) and rapid eye movement (REM) sleep without atonia (RSWA) have been proposed, but most of them were developed and validated on data recorded in the same clinic, thus they may be biased. This work aims to validate our data-driven algorithm for muscular activity detection during sleep, originally developed based on data recorded and manually scored at the Danish Center for Sleep Medicine. The validation was carried out on a cohort of 240 participants, including de novo Parkinson's disease (PD) patients and neurologically healthy controls, whose sleep data were recorded and manually evaluated at Paracelsus-Elena Klinik, Kassel, Germany. In the German cohort, the algorithm showed generally good agreement between manual and automated PLMS indices, and identified with 88.75% accuracy participants with PLMS index above 15 PLMS per hour of sleep, and with 84.17% accuracy patients suffering from REM sleep behaviour disorder (RBD) showing RSWA. By comparing the algorithm performances in the Danish and German cohorts, we hypothesized that inter-clinical differences may exist in the way limb movements are manually scored and how healthy controls are defined. Finally, the algorithm performed worse in PD patients, probably as a result of increased artefacts caused by abnormal motor events related to neurodegeneration. Our algorithm can identify, with reasonable performance, participants with RBD and increased PLMS index from data recorded in different centres, and its application may reveal inter clinical differences, which can be overcome in the future by applying automated methods.

Rapid eye movements are reduced in blind individuals.

There is ongoing controversy regarding the role of rapid eye movements (EMs) during rapid eye movement (REM) sleep. One prevailing hypothesis is that EMs during REM sleep are indicative of the presence of visual imagery in dreams. We tested the validity of this hypothesis by measuring EMs in blind subjects and correlating these with visual dream content. Eleven blind subjects, of whom five were congenitally blind (CB) and six late blind (LB), and 11 matched sighted control (SC) subjects participated in this study. All participants underwent full-night polysomnography (PSG) recordings that were staged manually following American Academy of Sleep Medicine (AASM) criteria. Nocturnal EMs were detected automatically using a validated EM detector, and EM activity was represented as "EM coverage" computed as percentage of time with EM in each sleep stage. Frequency of sensory dream elements was measured in dream recall questionnaires over a 30-day period. Both blind groups showed less EM coverage during wakefulness, N1, N2 and REM sleep than did controls. CB and LB subjects did not differ in EM activity. Validation of the detector applied to blind subjects revealed an overall accuracy of 95.6 ± 3.6%. Analysis of dream reports revealed that LB subjects reported significantly more visual dream elements than did CB. Although no specific mechanisms can be revealed in the current study, the quasi absence of nocturnal EMs in LB subjects despite preserved visual dream content does not support the visual scanning of dreams hypothesis. Specifically, results suggest a dissociation between EMs and visual dream content in blind individuals.

A Clinically Applicable Interactive Micro and Macro-Sleep Staging Algorithm for Elderly and Patients with Neurodegeneration.

Elderly and patients with neurodegenerative diseases (NDD) often complain about sleep problems and show altered sleep structure. Automated algorithms for efficient and specific sleep staging are needed. We propose a new algorithm using only one electroencephalographic and two electrooculographic channels to score wakefulness, rapid eye movement (REM) sleep and non-REM sleep in a cohort of elderly healthy controls (HC), patients with Parkinson's disease (PD), isolated REM sleep behavior disorder (iRBD), considered the prodromal stage of PD, and patients with PD and RBD (PD+RBD). The proposed method scores both standard 30-s epochs (macro-staging) and 5-s mini-epochs (micro-staging), whose evaluation may help to better understand sleep micro-structure. Moreover, the algorithm is interactive, as it labels the classified sleep epochs as either certain or uncertain, so that experts can manually review the uncertain ones. The algorithm performances were evaluated for macro-sleep staging, where it achieved overall accuracies of 0.87±0.05 in 41 HC, 0.86±0.10 in 57 PD, 0.76±0.10 in 31 iRBD and 0.77±0.10 in 30 PD+RBD patients when all 30-s epochs were considered. The accuracies increased to 0.91±0.05, 0.90±0.08, 0.85±0.09, 0.88±0.08 respectively when considering only the certain ones. The epochs labeled as uncertain were 9.95±4.15%, 11.13±7.86%, 18.39±7.38% and 18.90±8.00% in HC, PD, iRBD and PD+RBD respectively. The proposed interactive micro and macro sleep staging algorithm can be used in clinics to reduce the burden of manual sleep staging in elderly and patients with NDD.

Investigation of sleep spindle activity and morphology as predictors of neurocognitive functioning in medicated patients with schizophrenia.

Neurocognitive impairment is a trait marker of schizophrenia, but no effective treatment has yet been identified. Sleep spindle deficits have been associated with diminished sleep-dependent memory learning. We examined whether this link could be extended into various cognitive domains by investigating the association of a neurocognitive test battery (the Brief Assessment of Cognition in Schizophrenia) with sleep spindle activity and morphology. We examined 37 outpatients diagnosed with schizophrenia and medicated with both antipsychotics and benzodiazepines. Participants underwent 1 night polysomnography and test of neurocognitive functioning. We identified and analysed sleep spindles in all non-rapid eye movement sleep and in non-rapid eye movement sleep stage 2 in a central electroencephalography channel using an automatic sleep spindle detector previously validated. Slow sleep spindle density was computed from a frontal electroencephalography channel as well. We found no association between cognitive functioning and sleep spindle density or sleep spindle morphology for spindles in non-rapid eye movement sleep when controlling for gender, age, symptom severity, and daily dose of antipsychotics and benzodiazepines. For spindles in non-rapid eye movement sleep stage 2, we found that motor speed was associated with frontal slow sleep spindle density. We conclude that frontal slow spindle density might predict motor speed in medicated patients with schizophrenia, but that no other sleep spindle activity or sleep spindle morphology measures were predictors of neurocognitive functioning.

Validation of a new data-driven automated algorithm for muscular activity detection in REM sleep behavior disorder.

BACKGROUND: Documentation of REM sleep without atonia is fundamental for REM sleep behavior disorder (RBD) diagnosis. The automated REM atonia index (RAI), Frandsen index (FRI) and Kempfner index (KEI) were proposed for this, but achieved moderate performances. NEW METHOD: Using sleep data from 27 healthy controls (C), 29 RBD patients and 36 patients with periodic limb movement disorder (PLMD), we developed and validated a new automated data-driven method for identifying movements in chin and tibialis electromyographic (EMG) signals. A probabilistic model of atonia from REM sleep of controls was defined and movements identified as EMG areas having low likelihood of being atonia. The percentages of movements and the median inter-movement distance during REM and non-REM (NREM) sleep were used for distinguishing C, RBD and PLMD by combining three optimized classifiers in a 5-fold cross-validation scheme. RESULTS: The proposed method achieved average overall validation accuracies of 70.8% and 61.9% when REM and NREM, and only REM features were used, respectively. After removing apnea and arousal-related movements, they were 64.2% and 59.8%, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed RAI, FRI and KEI in identifying RBD patients and in particular achieved higher accuracy and specificity for classifying RBD. CONCLUSIONS: The results show that i) the proposed method has higher performances than the previous ones in distinguishing C, RBD and PLMD patients, ii) removal of apnea and arousal-related movements is not required, and iii) RBD patients can be better identified when both REM and NREM muscular activities are considered.

Automatic sleep classification using adaptive segmentation reveals an increased number of rapid eye movement sleep transitions.

The reference standard for sleep classification uses manual scoring of polysomnography with fixed 30-s epochs. This limits the analysis of sleep pattern, structure and, consequently, detailed association with other physiologic processes. We aimed to improve the details of sleep evaluation by developing a data-driven method that objectively classifies sleep in smaller time intervals. Two adaptive segmentation methods using 3, 10 and 30-s windows were compared. One electroencephalographic (EEG) channel was used to segment into quasi-stationary segments and each segment was classified using a multinomial logistic regression model. Classification features described the power in the clinical frequency bands of three EEG channels and an electrooculographic (EOG) anticorrelation measure for each segment. The models were optimised using 19 healthy control subjects and validated on 18 healthy control subjects. The models obtained overall accuracies of 0.71 ± 0.09, 0.74 ± 0.09 and 0.76 ± 0.08 on the validation data. However, the models allowed a more dynamic sleep, which challenged a true validation against manually scored hypnograms with fixed epochs. The automated classifications indicated an increased number of stage transitions and shorter sleep bouts using models with smaller window size compared with the hypnograms. An increased number of transitions from rapid eye movement (REM) sleep was likewise expressed in the model using 30-s windows, indicating that REM sleep has more fluctuations than captured by today's standard. The models developed are generally applicable and may contribute to concise sleep structure evaluation, research in sleep control and improved understanding of sleep and sleep disorders. The models could also contribute to objective measuring of sleep stability.

Probabilistic Data-Driven Method for Limb Movement Detection during Sleep.

Periodic limb movement disorder (PLMD) is a sleep disorder characterized by repetitive limb movements (LM) during night. The gold standard for LM detection consists of visual analysis of tibialis left (TIBL) and right (TIBR) electromyographic (EMG) signals. Such analysis is subjective and time-consuming. We here propose a semi-supervised and data-driven approach for LM detection during sleep that was trained and tested on 27 healthy controls (C) and 36 PLMD patients. After preprocessing of the EMG signals, discrete wavelet transform (Daubechies 4 mother wavelet and down to 4th decomposition level) was applied. EMG was reconstructed for each set of detail coefficients, thus obtaining four signals (DI-D4). The pre-processed EMG and DI-D4 signals were divided in 3-s mini-epochs of which traditional EMG features were calculated. Based on the assumption of lack of movements in healthy controls during rapid eye movement (REM) sleep, we used the features during REM of a subgroup of C to build a non-parametric probabilistic model defining the resting EMG distribution. This model was then used to classify the remaining mini-epochs as either resting EMG or LM. The percentages of 3-s mini-epochs with LMs were calculated for each subject and used to distinguish the remaining C and PLMD with a support vector machine and 5-fold cross validation scheme. Results showed that C can be distinguished by PLMD with accuracy higher than 82% in the preprocessed EMG and DI-D3 signals.

Comparison of computerized methods for rapid eye movement sleep without atonia detection.

Rapid eye movement (REM) sleep without atonia detection is a prerequisite for diagnosis of REM sleep behavior disorder (RBD). As the visual gold standard method is time-consuming and subjective, several automated methods have been proposed. This study aims to compare their performances: The REM atonia index (RAI), the supra-threshold-REM-activity metric, the Frandsen index, the short/long muscle activity indices, and the Kempfner index algorithms were applied to 27 healthy control participants (C), 25 patients with Parkinson's disease (PD) without RBD (PD-RBD), 29 patients with PD and RBD (PD + RBD), 29 idiopathic patients with RBD, and 36 patients with periodic limb movement disorder (PLMD). The indices were calculated in various configurations: (1) considering all muscle activities; (2) excluding the ones related to arousals; (3) excluding the ones during apnea events; (4) excluding the ones before and after apnea events; (5) combining configurations 2 and 3; and (6) combining configurations 2 and 4. For each of these configurations, the discrimination capability of the indices was tested for the following comparisons: (1) (C, PD-RBD, PLMD) vs (PD + RBD, RBD); (2) C vs RBD; (3) PLMD vs RBD; (4) C vs PD-RBD; (5) C vs PLMD; (6) PD-RBD vs PD + RBD; and (7) C vs PLMD vs RBD. Results showed varying methods' performances across the different configurations and comparisons, making it impossible to identify the optimal method and suggesting the need of further improvements. Nevertheless, RAI seems the most sensible one for RBD detection. Moreover, apnea and arousal-related movements seem not to influence the algorithms' performances in patients' classification.

Preserved sleep microstructure in blind individuals.

The loss of vision, particularly when it occurs early in life, is associated with compensatory cortical plasticity not only in the visual cortical areas, but throughout the entire brain. The absence of visual input to the retina can also induce changes in entrainment of the circadian rhythm, as light is the primary zeitgeber of the master biological clock found in the suprachiasmatic nucleus of the hypothalamus. In addition, a greater number of sleep disturbances is often reported in blind individuals. Here, we examined various electroencephalographic microstructural components of sleep, both during rapid-eye-movement (REM) sleep and non-REM (NREM) sleep, between blind individuals, including both of early and late onset, and normal-sighted controls. During wakefulness, occipital alpha oscillations were lower, or absent in blind individuals. During sleep, differences were observed across electrode derivations between the early and late blind samples, which may reflect altered cortical networking in early blindness. Despite these differences in power spectra density, the electroencephalography microstructure of sleep, including sleep spindles, slow wave activity, and sawtooth waves, remained present in the absence of vision.

Novel method for evaluation of eye movements in patients with narcolepsy.

BACKGROUND: Narcolepsy causes abnormalities in the control of wake-sleep, non-rapid-eye-movement (non-REM) sleep and REM sleep, which includes specific eye movements (EMs). In this study, we aim to evaluate EM characteristics in narcolepsy as compared to controls using an automated detector. METHODS: We developed a data-driven method to detect EMs during sleep based on two EOG signals recorded as part of a polysomnography (PSG). The method was optimized using the manually scored hypnograms from 36 control subjects. The detector was applied on a clinical sample with subjects suspected for central hypersomnias. Based on PSG, multiple sleep latency test and cerebrospinal fluid hypocretin-1 measures, they were divided into clinical controls (N = 20), narcolepsy type 2 (NT2, N = 19), and narcolepsy type 1 (NT1, N = 28). We investigated the distribution of EMs across sleep stages and cycles. RESULTS: NT1 patients had significantly less EMs during wake, N1, and N2 sleep and more EMs during REM sleep compared to clinical controls, and significantly less EMs during wake and N1 sleep compared to NT2 patients. Furthermore, NT1 patients showed less EMs during NREM sleep in the first sleep cycle and more EMs during NREM sleep in the second sleep cycle compared to clinical controls and NT2 patients. CONCLUSIONS: NT1 patients show an altered distribution of EMs across sleep stages and cycles compared to NT2 patients and clinical controls, suggesting that EMs are directly or indirectly controlled by the hypocretinergic system. A data-driven EM detector may contribute to the evaluation of narcolepsy and other disorders involving the control of EMs.

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

Sleep spindle alterations in patients with Parkinson's disease.

UNLABELLED: The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinson's disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. CONTROLS: We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

BACKGROUND: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases. NEW METHOD: This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model. RESULTS: The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients. COMPARISON WITH EXISTING METHOD: The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration. CONCLUSIONS: This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.