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. 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.

Detection and Characterization of Walking Bouts Using a Single Wrist-Worn Accelerometer in Free-living Conditions.

Detection and characterization of abnormalities of movement are important to develop a method for detecting early signs of Parkinson's disease (PD). Most of the current research in detection of characteristic reduction of movements due to PD, known as parkinsonism, requires using a set of invasive sensors in a clinical or controlled environment. Actigraphy has been widely used in medical research as a non-invasive data acquisition method in free-living conditions for long periods of time. The proposed algorithm uses triaxial accelerometer data obtained through actigraphy to detect walking bouts at least 10 seconds long and characterize them using cadence and arm swing. Accurate detection of walking periods is the first step toward the characterization of movement based on gait abnormalities. The algorithm was based on a Walking Score (WS) derived using the value of the auto-correlation function (ACF) for the Resultant acceleration vector. The algorithm achieved a precision of 0.90, recall of 0.77, and F1 score of 0.83 compared to the expert scoring for walking bout detection. We additionally described a method to measure arm swing amplitude.

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. 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.

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. 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.

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., discovery, replication, and joint meta-analysis) of four cohorts (MrOS, the Wisconsin Sleep Cohort Study, HypnoLaus, and MESA), comprised of 6843 total subjects. METHODS: The MrOS study and Wisconsin Sleep Cohort Study (N = 1745 cases) were used for discovery. Replication in the HypnoLaus and MESA cohorts (1002 cases) preceded joint meta-analysis. We also performed LD score regression, estimated heritability, and computed genetic correlations between potentially associated traits such as restless leg syndrome (RLS) and insomnia. The causality and direction of the relationships between PLMS and RLS was evaluated using Mendelian randomization. RESULTS: We found 2 independent loci were significantly associated with PLMS: rs113851554 (p = 3.51 × 10-12, ß = 0.486), an SNP located in a putative regulatory element of intron eight of MEIS1 (2p14); and rs9369062 (p = 3.06 × 10-22, ß = 0.2093), a SNP located in the intron region of BTBD9 (6p12); both of which were also lead signals in RLS GWAS. PLMS is genetically correlated with insomnia, risk of stroke, and RLS, but not with iron deficiency. Pleiotropy adjusted Mendelian randomization analysis identified a causal effect of RLS on PLMS. CONCLUSIONS: Because PLMS is more common than RLS, PLMS may have multiple causes and additional studies are needed to further validate these findings.

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. 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.

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

[This corrects the article DOI: 10.2196/35696.].

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. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209.

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. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

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.

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). 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.

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. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.

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. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. RESULTS: In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075). CONCLUSIONS: The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL. SIGNIFICANCE: This study validates a fully automatic method for scoring arousals in PSGs.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BACKGROUND: Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span. OBJECTIVE: This interdisciplinary study intends to develop a biological age model and explore its usefulness. METHODS: The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course. RESULTS: We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021. CONCLUSIONS: We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

OBJECTIVE: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and validate an end-to-end deep learning system for the automatic scoring of LMs and PLMS in sleep. METHODS: The deep learning system was developed, validated and tested, with respect to manual annotations by expert technicians on 800 overnight PSGs using a leg electromyography channel. The study includes data from three cohorts, namely, the Wisconsin Sleep Cohort (WSC), Stanford Sleep Cohort (SSC) and MrOS Sleep Study. The performance of the system was further compared against individual expert technicians and existing PLM detectors. RESULTS: The system achieved an F1 score of 0.83, 0.71, and 0.77 for the WSC, SSC, and an ancillary study (Osteoporotic Fractures in Men Study, MrOS) cohorts, respectively. In a total of 60 PSGs from the WSC and the SSC scored by nine expert technicians, the system performed better than two and comparable to seven of the individual scorers with respect to a majority-voting consensus of the remaining scorers. In 60 PSGs from the WSC scored accurately for PLMS, the system outperformed four previous PLM detectors, which were all evaluated on the same data, with an F1 score of 0.85. CONCLUSIONS: The proposed system performs better or comparable to individual expert technicians while outperforming previous automatic detectors. Thereby, the study validates fully automatic methods for scoring LMs in sleep.