Monitoring sleep stages with a soft electrode array: Comparison against vPSG and home-based detection of REM sleep without atonia

Semi automatic quantification of REM sleep without atonia in natural sleep environment

Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson's disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

Too little or too much nocturnal movements in Parkinson's disease: A practical guide to managing the unseen

Nocturnal and sleep-related motor disorders in people with Parkinson's disease (PD) have a wide spectrum of manifestations and present a complex clinical picture. Problems can arise due to impaired movement ability (hypokinesias), e.g. nocturnal hypokinesia or early-morning akinesia, or to excessive movement (hyperkinesias), e.g. end-of-the-day dyskinesia, parasomnias, periodic limb movement during sleep and restless legs syndrome. These disorders can have a significant negative impact on the sleep, daytime functional ability, and overall quality of life of individuals with PD and their carers. The debilitating motor issues are often accompanied by a combination of non-motor symptoms, including pain and cramping, which add to the overall burden. Importantly, nocturnal motor disorders encompass a broader timeline than just the period of sleep, often starting in the evening, as well as occurring throughout the night and on awakening, and are not just limited to problems of insomnia or sleep fragmentation. Diagnosis can be challenging as, in many cases, the 'gold standard' assessment method is video polysomnography, which may not be available in all settings. Various validated questionnaires are available to support evaluation, and alternative approaches, using wearable sensors and digital technology, are now being developed to facilitate early diagnosis and monitoring. This review sets out the parameters of what can be considered normal nocturnal movement and describes the clinical manifestations, usual clinical or objective assessment methods, and evidence for optimal management strategies for the common nocturnal motor disorders that neurologists will encounter in people with PD in their clinical practice.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

BACKGROUND

Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

OBJECTIVES

The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.

METHODS

Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.

RESULTS

Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).

CONCLUSIONS

Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Bio-potential noise of dry printed electrodes: physiology versus the skin-electrode impedance

Objective. To explore noise characteristics and the effect physiological activity has on the link between impedance and noise.Approach. Dry-printed electrodes are emerging as a new and exciting technology for skin electro-physiology. Such electrode arrays offer many advantages including user convenience, quick placement, and high resolution. Here we analyze extensive electro-physiological data recorded from the arm and the face to study and quantify the noise of dry electrodes, and to characterize the link between noise and impedance. In particular, we studied the effect of the physiological state of the subject (e.g. rapid eye movement sleep) on noise.Main results. We show that baseline noise values extracted from dry electrodes in the arm are in agreement with the Nyquist equation. In the face, on the other hand, the measured noise values were higher than the values predicted by the Nyquist equation. In addition, we studied how different electrode properties affect performances, including electrode size, shape, and material properties.Significance. Altogether, the results presented here provide a basis for understanding dry electrode performances and substantiate their great potential in electro-physiological investigations.