Automated polysomnographic assessment for rapid eye movement sleep behavior disorder

Methods and systems for diagnosing or assessing rapid eye movement sleep behavior disorder (RBD). Muscle tone or activity variance during rapid eye movement (REM) and nonrapid eye movement (NREM) sleep intervals of a polysom- nogram are compared. A threshold based on the NREM data is used to identify a subj ect-specific threshold for abnormality in the REM variance. A metric that includes the percentage of REM variance exceeding the threshold relates to RBD.https://digitalcommons.mtu.edu/patents/1017/thumbnail.jp

A comparative study of four novel sleep apnoea episode prediction systems

The prediction of sleep apnoea and hypopnoea episodes could allow treatment to be applied before the event be-comes detrimental to the patients sleep, and for a more spe-cific form of treatment. It is proposed that features extracted from breaths preceding an apnoea and hypopnoea could be used in neural networks for the prediction of these events. Four different predictive systems were created, processing the nasal airflow signal using epoching, the inspiratory peak and expiratory trough values, principal component analysis (PCA) and empirical mode decomposition (EMD). The neu-ral networks were validated with naïve data from six over-night polysomnographic records, resulting in 83.50% sensi-tivity and 90.50% specificity. Reliable prediction of apnoea and hypopnoea is possible using the epoched flow and EMD of breaths preceding the event

APAP titration in patients with mild to moderate OSAS and periodic limb movement syndrome

(APAP) titration in a partially attended setting; 2) to verify whether APAP performance depends on the apneahypopnea and periodic limb movement indexes (PLMI). Methods. 65 CPAP naïve subjects with a sleep disorder of breathing and daytime sleepiness underwent a standard polysomnography (first night), APAP titration (second night, partially attended), and a standard polysomnography using continuous positive airway pressure (CPAP) at the effective pressure (Peff) established from the APAP titration (third night) in a sleep disorder laboratory in a 400-bed community hospital. We examined the apnea-hypopnea index (AHI), sleep stages, arousals induced by respiratory events (RESPa) and PLM (PLMa), and oxygen saturation during the first and third nights on CPAP at the Peff. Patients were divided into three groups according to their AHI and PLMI. Results. At the Peff defined using APAP on the third night, the mean AHI dropped from 29.6 ± 21.8 to 3.1 ± 3.4, and the RESPa index from 16.5 ± 16.2 to 1.7 ± 2.6. No differences emerged in sleep stages or spontaneous arousals (first vs third night). Overall, 92% of the patients met the standard for an acceptable outcome of positive pressure titration. Baseline AHI and PLMI did not affect the outcome of titration. Conclusions. In patients with mild to moderate OSAS and PLMS, APAP titration enables the optimal fixed pressure for CPAP home therapy to be determined in at least 90% of patients

Inter-expert and intra-expert reliability in sleep spindle scoring

Objectives: To measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings. Methods: The EEG dataset was comprised of 400 randomly selected 115 s segments of stage 2 sleep from 110 sleeping subjects in the general population (57 ± 8, range: 42–72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F_1-scores, Cohen’s kappa (κ), and intra-class correlation coefficient (ICC). Results: We found an average intra-expert F_1-score agreement of 72 ± 7% (κ: 0.66 ± 0.07). The average inter-expert agreement was 61 ± 6% (κ: 0.52 ± 0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system. Conclusions: We estimate that 2–3 experts are needed to build a spindle scoring dataset with ‘substantial’ reliability (κ: 0.61–0.8), and 4 or more experts are needed to build a dataset with ‘almost perfect’ reliability (κ: 0.81–1). Significance: Spindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system

Neurophysiological basis of rapid eye movement sleep behavior disorder:Informing future drug development

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by a history of recurrent nocturnal dream enactment behavior and loss of skeletal muscle atonia and increased phasic muscle activity during REM sleep: REM sleep without atonia. RBD and associated comorbidities have recently been identified as one of the most specific and potentially sensitive risk factors for later development of any of the alpha-synucleinopathies: Parkinson’s disease, dementia with Lewy bodies, and other atypical parkinsonian syndromes. Several other sleep-related abnormalities have recently been identified in patients with RBD/Parkinson’s disease who experience abnormalities in sleep electroencephalographic frequencies, sleep–wake transitions, wake and sleep stability, occurrence and morphology of sleep spindles, and electrooculography measures. These findings suggest a gradual involvement of the brainstem and other structures, which is in line with the gradual involvement known in these disorders. We propose that these findings may help identify biomarkers of individuals at high risk of subsequent conversion to parkinsonism

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

Huomaamattomat mittausmenetelmät unen laadun tarkkailussa

Sleep is an important part of health and well-being. While sleep quantity is directly measurable, sleep quality has traditionally been assessed with subjective methods such as questionnaires. The study of sleep disorders has for a long time been confined to clinical environments, and patients have had to endure cumbersome procedures involving multiple electrodes placed on the body. Recent developments in sensor technology as well as data analysis methods have enabled continuous, unobtrusive sleep data recording in the home environment. This has opened new possibilities for studying various sleep parameters and their effect on the quality of sleep. This thesis consists of two parts. The first part is a literature review examining the field of sleep quality research with focus on the application of intelligent methods and signal processing. The second part is a descriptive data analysis look at sleep data obtained with non-invasive sensors.Uni on terveyden ja hyvinvoinnin keskeinen tekijä. Unen määrä on helposti mitattavissa, mutta unen laatua on perinteisesti seurattu kyselylomakkeiden kaltaisin subjektiivisin menetelmin. Unihäiriöiden tutkiminen on pitkään rajoittunut kliinisiin ympäristöihin, ja potilaiden on täytynyt sietää hankalia tutkimusmenetelmiä useine kehoon kiinnitettävine elektrodeineen. Anturiteknologian ja data-analyysimenetelmien kehittyminen on mahdollistanut unidatan jatkuvan ja huomaamattoman tallentamisen kotiympäristössä. Tämä on avannut uusia mahdollisuuksia sekä unen ominaisuuksien että niiden unen laatuun vaikuttavien tekijöiden tutkimiselle. Tämä tutkimus jakautuu kahteen osaan. Ensimmäinen osa on kirjallisuuskatsaus unen laadun tutkimukseen, painopisteenä älykkäiden menetelmien ja signaalinkäsittelyn soveltaminen. Toisessa osassa esitellään huomaamattomilla sensoreilla kerättävän unidatan tutkimista ja sen deskriptiivistä data-analyysiä, esimerkkinä ballistokardiografia

EEG arousal prediction via hypoxemia indicator in patients with Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is a sleep breathing disorder characterized by recurrent airflow obstruction caused by a total or partial collapse of the upper airway. OSAS is a common affliction suffered by millions. The arousal index (ArI) is the best predictor of daytime somnolence for patients with OSAS, however, the polysomnography (PSG) examination in the sleep lab is expensive, time consuming and labor intensive. The objective of this study is to evaluate the ability and reliability of arousal prediction via the hypoxemia indicator in patients with OSAS. Patients with a diagnosis of OSAS by standard polysomnography were recruited from China Medical University Hospital Centre. There were 248 patients in the learning set and 255 patients in the validation set. The presence of OSAS was defined as an Apnea Hypopnea Index (AHI) >5/h. We used the hypoxemia indicator to predict ArI in patients with OSAS by linear regression and evaluated the prediction performance in different clinical characteristics subsets. The standard error of estimate of ArI prediction was 12.9 in the learning set. For predicting the severity of ArI, for ArI exceeding 15/h or 30/h, the sensitivity was 53.4% and 75.7%, respectively, with corresponding specificity of 96.6%, and 77.4%, respectively. We analyzed the hypoxemia indicator for predicting the severity of sleep fragmentation. The result demonstrated it ispossible to predict ArI via the hypoxemia indicator, especially in severepatients