Role of sleep in neurodegeneration: the consensus report of the 5th Think Tank World Sleep Forum

Sleep abnormalities may represent an independent risk factor for neurodegeneration. An international expert group convened in 2021 to discuss the state-of-the-science in this domain. The present article summarizes the presentations and discussions concerning the importance of a strategy for studying sleep- and circadian-related interventions for early detection and prevention of neurodegenerative diseases. An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years; discussed the current challenges in the field of relationships among sleep, sleep disorders, and neurodegeneration; and identified future priorities. Sleep efficiency and slow wave activity during non-rapid eye movement (NREM) sleep are decreased in cognitively normal middle-aged and older adults with Alzheimer's disease (AD) pathology. Sleep deprivation increases amyloid-β (Aβ) concentrations in the interstitial fluid of experimental animal models and in cerebrospinal fluid in humans, while increased sleep decreases Aβ. Obstructive sleep apnea (OSA) is a risk factor for dementia. Studies indicate that positive airway pressure (PAP) treatment should be started in patients with mild cognitive impairment or AD and comorbid OSA. Identification of other measures of nocturnal hypoxia and sleep fragmentation could better clarify the role of OSA as a risk factor for neurodegeneration. Concerning REM sleep behavior disorder (RBD), it will be crucial to identify the subset of RBD patients who will convert to a specific neurodegenerative disorder. Circadian sleep-wake rhythm disorders (CSWRD) are strong predictors of caregiver stress and institutionalization, but the absence of recommendations or consensus statements must be considered. Future priorities include to develop and validate existing and novel comprehensive assessments of CSWRD in patients with/at risk for dementia. Strategies for studying sleep-circadian-related interventions for early detection/prevention of neurodegenerative diseases are required. CSWRD evaluation may help to identify additional biomarkers for phenotyping and personalizing treatment of neurodegeneration.

Exploring the Association Linking Head Position and Sleep Architecture to Motor Impairment in Parkinson's Disease: An Exploratory Study

Patients with Parkinson's disease (PD) tend to sleep more frequently in the supine position and less often change head and body position during sleep. Besides sleep quality and continuity, head and body positions are crucial for glymphatic system (GS) activity. This pilot study evaluated sleep architecture and head position during each sleep stage in idiopathic PD patients without cognitive impairment, correlating sleep data to patients' motor and non-motor symptoms (NMS). All patients underwent the multi-night recordings, which were acquired using the Sleep Profiler headband. Sleep parameters, sleep time in each head position, and percentage of slow wave activity (SWA) in sleep, stage 3 of non-REM sleep (N3), and REM sleep in the supine position were extracted. Lastly, correlations with motor impairment and NMS were performed. Twenty PD patients (65.7 ± 8.6 y.o, ten women) were included. Sleep architecture did not change across the different nights of recording and showed the prevalence of sleep performed in the supine position. In addition, SWA and N3 were more frequently in the supine head position, and N3 in the supine decubitus correlated with REM sleep performed in the same position; this latter correlated with the disease duration (correlation coefficient = 0.48, p-value = 0.03) and motor impairment (correlation coefficient = 0.53, p-value = 0.02). These preliminary results demonstrated the importance of monitoring sleep in PD patients, supporting the need for preventive strategies in clinical practice for maintaining the lateral head position during the crucial sleep stages (SWA, N3, REM), essential for permitting the GS function and activity and ensuring brain health.

Transferability of a Sensing Mattress for Posture Classification from Research into Clinics

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

REM Sleep Behaviour Disorder, a narrative review from a technological perspective

STUDY OBJECTIVES

Isolated REM sleep behaviour disorder (iRBD) is a parasomnia characterized by dream enactment. It represents a prodromal state of alpha-synucleinopathies, like Parkinson's disease. In recent years, biomarkers of increased risk of phenoconversion from iRBD to overt alpha-synucleinopathies have been identified. Currently, diagnosis and monitoring rely on subjective reports and polysomnography performed in the sleep lab, which is limited in availability and cost intensive. Wearable technologies and computerized algorithms may provide comfortable and cost-efficient means to not only improve the identification of iRBD patients but also to monitor risk factors of phenoconversion. In this work, we review studies using these technologies to identify iRBD or monitor phenoconversion biomarkers.

METHODS

A review of articles published until 31st May 2022 using the Medline database was performed. We included only papers in which subjects with RBD were part of the study population. The selected papers were divided into four sessions: actigraphy, gait analysis systems, computerized algorithms, and novel technologies.

RESULTS

25 articles were included in the review. Actigraphy, wearable accelerometers, pressure mats, smartphones, tablets, and algorithms based on polysomnography signals were used to identify RBD and monitor the phenoconversion. Rest-activity patterns, core body temperature, gait, and sleep parameters were able to identify the different stages of the disease.

CONCLUSIONS

These tools may complement current diagnostic systems in the future, providing objective ambulatory data obtained comfortably and inexpensively. Consequently, screening for iRBD and follow-up will be more accessible for the concerned patient cohort.

Label noise and self-learning label correction in cardiac abnormalities classification

OBJECTIVE

Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise owing to inter- and intra-observer variability, and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals.

APPROACH

A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets.

MAIN RESULTS

Under high levels of noise, the cross-validation results of self-learning label correction showed an improvement of approximately 3% in the Challenge score for the PhysioNet/CinC 2021 Challenge dataset and, an improvement in accuracy of 5$\%$ and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.

Wearables in the home-based assessment of abnormal movements in Parkinson's disease: a systematic review of the literature

At present, the standard practices for home-based assessments of abnormal movements in Parkinson's disease (PD) are based either on subjective tools or on objective measures that often fail to capture day-to-day fluctuations and long-term information in real-life conditions in a way that patient's compliance and privacy are secured. The employment of wearable technologies in PD represents a great paradigm shift in healthcare remote diagnostics and therapeutics monitoring. However, their applicability in everyday clinical practice seems to be still limited. We carried out a systematic search across the Medline Database. In total, 246 publications, published until 1 June 2020, were identified. Among them, 26 reports met the inclusion criteria and were included in the present review. We focused more on clinically relevant aspects of wearables' application including feasibility and efficacy of the assessment, the number, type and body position of the wearable devices, type of PD motor symptom, environment and duration of assessments and validation methodology. The aim of this review is to provide a systematic overview of the current knowledge and state-of-the-art of the home-based assessment of motor symptoms and fluctuations in PD patients using wearable technology, highlighting current problems and laying foundations for future works.

Measuring Sleep, Wakefulness, and Circadian Functions in Neurologic Disorders

Neurologic disorders impact the ability of the brain to regulate sleep, wake, and circadian functions, including state generation, components of state (such as rapid eye movement sleep muscle atonia, state transitions) and electroencephalographic microarchitecture. At its most extreme, extensive brain damage may even prevent differentiation of sleep stages from wakefulness (eg, status dissociatus). Given that comorbid sleep-wake-circadian disorders are common and can adversely impact the occurrence, evolution, and management of underlying neurologic conditions, new technologies for long-term monitoring of neurologic patients may potentially usher in new diagnostic strategies and optimization of clinical management.