Temporal and sleep stage-dependent agreement in manual scoring of respiratory events.

Obstructive sleep apnea diagnosis is based on the manual scoring of respiratory events. The agreement in the manual scoring of the respiratory events lacks an in-depth investigation as most of the previous studies reported only the apnea-hypopnea index or overall agreement, and not temporal, second-by-second or event subtype agreement. We hypothesized the temporal and subtype agreement to be low because the event duration or subtypes are not generally considered in current clinical practice.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.

BreathFinder: A Method for Non-Invasive Isolation of Respiratory Cycles Utilizing the Thoracic Respiratory Inductance Plethysmography Signal.

Introduction: The field of automatic respiratory analysis focuses mainly on breath detection on signals such as audio recordings, or nasal flow measurement, which suffer from issues with background noise and other disturbances. Here we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal using the non-invasive signal of the respiratory inductance plethysmography belts.

Purpose: The algorithm locates breaths using signal processing and statistical methods on the thoracic respiratory inductance plethysmography belt and enables the analysis of sleep data on an individual breath level.

Feasibility and usability of three consecutive nights with self-applied, home polysomnography.

In-laboratory polysomnography, the gold-standard for diagnosing sleep disorders, is resource-demanding and not conducive to multiple night evaluations. Ambulatory polysomnography, especially when self-applied, could be a viable alternative. This study aimed to assess the feasibility and reliability of self-applied polysomnography over three consecutive nights in untrained participants, assessing: technical success rate; comparing sleep diagnostic variables from single and multiple nights; and evaluating participants' subjective experience.

Prolonged lung-to-finger circulation time indicates an increased risk of intermittent hypoxaemia in sleep apnoea patients.

Introduction: Intermittent hypoxaemia is closely associated with cardiovascular dysfunction and may be a more accurate indicator of obstructive sleep apnoea (OSA) severity than conventional metrics. Another key factor is the lung-to-finger circulation time (LFCt), defined as the duration from the cessation of a respiratory event to the lowest point of oxygen desaturation. LFCt serves as a surrogate marker for circulatory delay and is linked with cardiovascular function.

The training times of athletes could play a role in clinical sleep problems due to their associations with sleep difficulty scores.

Objectives: Sleep is a key component of athletic recovery, yet training times could influence the sleep of athletes. The aim of the current study was to compare sleep difficulties in athletes across different training time groups (early morning, daytime, late evening, early morning plus late evening) and to investigate whether training time can predict sleep difficulties.

Methods: Athletes from various sports who performed at a national-level (n = 273) answered the Athlete Sleep Screening Questionnaire (ASSQ) along with several other questionnaires related to demographics, exercise training, and mental health.

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight.

Introduction: Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers.

The role of physical activity on obstructive sleep apnea severity and hypoxic load, and the mismatch between subjective and objective physical activity assessments.

Obesity is the primary risk factor for the development of obstructive sleep apnea, and physical inactivity plays an important role. However, most studies have either only evaluated physical activity subjectively or objectively in obstructive sleep apnea. The objectives of this study were: (i) to assess the relationship between obstructive sleep apnea severity (both apnea-hypopnea index and desaturation parameters) and both objectively and subjectively measured physical activity after adjustment for anthropometry and body composition parameters; and (ii) to assess the relationship between objective and subjective physical activity parameters and whether obstructive sleep apnea severity has a modulatory effect on this relationship.

Guidelines for the development, performance evaluation and validation of new sleep technologies (DEVSleepTech guidelines) - a protocol for a Delphi consensus study.

New sleep technologies are being developed, refined and delivered at a fast pace. However, there are serious concerns about the validation and accuracy of new sleep-related technologies being made available, as many of them, especially consumer-sleep technologies, have not been tested in comparison with gold-standard methods or have been approved by health regulatory agencies. The importance of proper validation and performance evaluation of new sleep technologies has already been discussed in previous studies and some recommendations have already been published, but most of them do not employ standardized methodology and are not able to cover all aspects of new sleep technologies.

Multi-centre arousal scoring agreement in the Sleep Revolution.

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs).

Review and perspective on sleep-disordered breathing research and translation to clinics.

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed.

The European Insomnia Guideline: An update on the diagnosis and treatment of insomnia 2023.

Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential-diagnostic purposes (A).

The economic cost of obstructive sleep apnea: A systematic review.

Obstructive sleep apnea (OSA) is a common disease associated with a high prevalence of costly comorbidities and accidents that add to the disease's economic impact. Although more attention has been focused on OSA in recent years, no previous systematic reviews have synthesized findings from existing studies that provide estimates of the economic cost of OSA. This study aims to summarize the findings of existing studies that provide estimates of the cost of OSA.

Evaluating User Compliance in Mobile Health Apps: Insights from a 90-Day Study Using a Digital Sleep Diary.

Unlabelled: Sleep diaries are the gold standard for subjective assessment of sleep variables in clinical practice. Digitization of sleep diaries is needed, as paper versions are prone to human error, memory bias, and difficulties monitoring compliance.

Methods: 45 healthy eligible participants (M = 50.

Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults.

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library).

Obstructive sleep apnea-related intermittent hypoxaemia is associated with impaired vigilance.

Obstructive sleep apnea (OSA)-related intermittent hypoxaemia is a potential risk factor for different OSA comorbidities, for example cardiovascular disease. However, conflicting results are found as to whether intermittent hypoxaemia is associated with impaired vigilance. Therefore, we aimed to investigate how desaturation characteristics differ between the non-impaired vigilance and impaired vigilance patient groups formed based on psychomotor vigilance task (PVT) performance and compared with traditional OSA severity parameters.

A Comparison of Signal Combinations for Deep Learning-Based Simultaneous Sleep Staging and Respiratory Event Detection.

Objective: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern.

Study Objectives: Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep.

Methods: Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation.