Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence.

Acoustic Stimulation to Improve Slow-Wave Sleep in Alzheimer's Disease: A Multiple Night At-Home Intervention.

Objectives: To investigate the efficacy of closed-loop acoustic stimulation (CLAS) during slow-wave sleep (SWS) to enhance slow-wave activity (SWA) and SWS in patients with Alzheimer's disease (AD) across multiple nights and to explore associations between stimulation, participant characteristics, and individuals' SWS response.

Design: A 2-week, open-label at-home intervention study utilizing the DREEM2 headband to record sleep data and administer CLAS during SWS.

Setting And Participants: Fifteen older patients with AD (6 women, mean age: 76.

Central sleep apnea: emphasizing recognition and differentiation.

Introduction: Central sleep apnea (CSA) is a sleep-related breathing disorder in which the effort to breathe is intermittently diminished or absent. CSA is a common disorder among patients with different cardiovascular disorders, including heart failure. In addition, a growing number of medications have been shown to induce CSA and CSA can emerge after initiation of treatment for obstructive sleep apnea.

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.

With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high and low confidence and reducing the risk of errors. This study introduces U-PASS, a specialized human-centered machine learning pipeline tailored for clinical applications, which effectively communicates uncertainty to clinical experts and collaborates with them to improve predictions.

Recovery from shift work.

One fifth of today's workforce is engaged in shift work and exposed to various mental and physical health risks including shift work disorder. Efficiently recovering from shift work through physical and mental interventions allows us to mitigate negative effects on health, enables a better work-life balance and enhances our overall wellbeing. The aim of this review is to provide a state-of-the-art overview of the available literature.

Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations.

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis.

Catathrenia in severe obstructive sleep apnea: A novel entity never described before.

Study Objectives: Catathrenia, derived from the Greek κατά (kata) meaning below and θρηνώ (threnia) to lament, is characterized by expiratory groaning episodes during sleep. In a case series of nine patients with severe obstructive sleep apnea, we observed a peculiar groaning entity that has not been described before.

Methods: We described and illustrated the cases with polysomnographic tracings and additional audio recordings.

Acoustic stimulation as a promising technique to enhance slow-wave sleep in Alzheimer's disease: results of a pilot study.

Study Objectives: Sleep disturbances are common in people with Alzheimer's disease (AD), and a reduction in slow-wave activity is the most striking underlying change. Acoustic stimulation has emerged as a promising approach to enhance slow-wave activity in healthy adults and people with amnestic mild cognitive impairment. In this phase 1 study we investigated, for the first time, the feasibility of acoustic stimulation in AD and piloted the effect on slow-wave sleep (SWS).

Insights into the Use of Point-of-Care Ultrasound for Diagnosing Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a sleeping disorder caused by complete or partial disturbance of breathing during the night. Existing screening methods include questionnaire-based evaluations which are time-consuming, vary in specificity, and are not globally adopted. Point-of-care ultrasound (PoCUS), on the other hand, is a painless, inexpensive, portable, and useful tool that has already been introduced for the evaluation of upper airways by anesthetists.

A late diagnosis of myasthenia gravis at the respiratory practice: back to basics.

https://bit.ly/3qMeWFd.

In search of a cut-off apnea-hypopnea index in type 3 home portable monitors to diagnose and treat obstructive sleep apnea: a mathematical simulation.

The American Academy of Sleep Medicine (AASM) uses similar apnea-hypopnea index (AHI) cut-off values to diagnose and define severity of sleep apnea independent of the technique used: in-hospital polysomnography (PSG) or type 3 portable monitoring (PM). Taking into account that PM theoretically might underestimate the AHI, we explored whether a lower cut-off would be more appropriate. We performed mathematical re-calculations on the diagnostic PSG-AHI (scored using AASM 1999 rules) of 865 consecutive patients with an AHI of ≥20 events/h who started continuous positive airway pressure (CPAP).

Short-term positive effects of a mandibular advancement device in a selected phenotype of patients with moderate obstructive sleep apnea: a prospective study.

Study Objectives: To evaluate (determinants of) treatment success of mandibular advancement device application in a selected phenotype of patients with obstructive sleep apnea (OSA).

Methods: Ninety nonobese patients with moderate OSA (obstructive apnea-hypopnea index [OAHI] ≥ 15 and < 30 events/h) without comorbidities were prospectively included. Polysomnography was performed at baseline and with a mandibular advancement device.

From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging.

The recent breakthrough of wearable sleep monitoring devices has resulted in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset.

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients.

Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required.

Capacitively-Coupled ECG and Respiration for Sleep-Wake Prediction and Risk Detection in Sleep Apnea Patients.

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep-wake using ccECG and ccBioZ, and detection of high-risk OSA patients.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared.

High adherence to continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea (OSA) in Belgium: a narrative review.

Objectives: Continuous positive airway pressure (CPAP) is the 'gold standard' treatment for moderate-to-severe obstructive sleep apnea (OSA); adherence is an important issue. The aim of this paper is to review Belgian data on CPAP users and their adherence over a period of 11 years.

Methods: Data delivered annually by the CPAP centers to the Belgian National Institute for Health Insurance (RIZIV/INAMI) were studied.

Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects.

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea.

Objective: The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea.

Approach: A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials.

Respiratory decline in adult patients with Becker muscular dystrophy: A longitudinal study.

Becker muscular dystrophy (BMD) is a rare hereditary neuromuscular disease, caused by a genetic defect in the Duchenne Muscular Dystrophy (DMD) gene. We studied the natural history of respiratory function and its affecting factors in 23 adult BMD patients. These important data are needed for (future) clinical trials in BMD but are largely lacking.

A critical review of the pharmacological treatment of REM sleep behavior disorder in adults: time for more and larger randomized placebo-controlled trials.

Rapid Eye Movement sleep behavior disorder (RBD) is a parasomnia causing sufferers to physically act out their dreams. These behaviors can disrupt sleep and sometimes lead to injuries in patients and their bed-partners. Clonazepam and melatonin are the first-line pharmacological treatment options for RBD based on direct uncontrolled clinical observations and very limited double-blind placebo-controlled trials.