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Sleep Medicine Aug 2021Existing data suggest that smoking may be associated with sleep disturbances. This study aimed to determine the association between smoking and both subjective and...
OBJECTIVES
Existing data suggest that smoking may be associated with sleep disturbances. This study aimed to determine the association between smoking and both subjective and objective sleep quality.
METHODS
Cross-sectional analysis of sleep characteristics in 3233 participants from the population-based CoLaus-HypnoLaus cohort (52.2% women, mean age 56.6 ± 10.2 years) who completed questionnaires on sleep quality, of whom 1489 (46%) had a full polysomnography. Smoking data were self-reported; participants were classified by smoking status as current, former or never smokers. Primary outcomes were subjective sleep quality assessed by sleep questionnaires, and objective sleep quality based on polysomnography (sleep macrostructure), including power spectral analysis of the electroencephalogram on C4 electrode (sleep microstructure), quantifying the relative amount of delta power (1-4 Hz), a marker of sleep depth, and arousal-associated alpha power (8-12 Hz).
RESULTS
Current smokers had a shift toward faster sleep electroencephalogram activity with lower delta power in non-REM sleep compared with former and never smokers (-2.8 ± 0.4% and -2.4 ± 0.4%, respectively; both p < 0.001) and higher alpha power (+0.8 ± 0.2%; p < 0.001) compared with never smokers. There was a dose-dependent negative association between electroencephalogram delta power and smoking intensity (r = -1.2 [-1.9, -0.5]; p = 0.001). Additionally, mean nocturnal oxygen saturation was lower in current smokers.
CONCLUSIONS
Current smokers had decreased objective sleep quality, with a dose-dependent association between smoking intensity and decrease in electroencephalogram delta power during non-REM sleep, in addition to an increase in alpha power. Considering the importance of sleep quality for wellbeing and health, these results provide further data to support smoking cessation.
Topics: Aged; Cross-Sectional Studies; Electroencephalography; Female; Humans; Male; Middle Aged; Polysomnography; Sleep; Smoking
PubMed: 34126401
DOI: 10.1016/j.sleep.2021.05.024 -
Sleep & Breathing = Schlaf & Atmung Sep 2022Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method...
OBJECTIVE
Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea-Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO signals using a large (n = 412) dataset serving as ground truth.
DESIGN
Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO (%)-signal only, and two additional models that use the respiratory features and the SpO (%) feature, one allowing a time lag of 30 s between the two signals.
RESULTS
Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO, respiration-only, and SpO-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively.
CONCLUSIONS
A wearable respiratory effort signal with or without SpO signal predicted AHI accurately, and best performance was achieved with using both signals.
Topics: Humans; Oxygen; Oxygen Saturation; Polysomnography; Respiratory Rate; Sleep Apnea Syndromes; Wearable Electronic Devices
PubMed: 34409545
DOI: 10.1007/s11325-021-02465-2 -
Sensors (Basel, Switzerland) Nov 2023Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the... (Review)
Review
Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.
Topics: Humans; Polysomnography; Sleep Apnea, Obstructive; Sleep; Sleep Apnea Syndromes; Oximetry
PubMed: 38067885
DOI: 10.3390/s23239512 -
Nursing ResearchA range of sleep disturbances and disorders are problematic in people after stroke; they interfere with recovery of function during poststroke rehabilitation. However,...
BACKGROUND
A range of sleep disturbances and disorders are problematic in people after stroke; they interfere with recovery of function during poststroke rehabilitation. However, studies to date have focused primarily on the effects of one sleep disorder-obstructive sleep apnea (OSA)-on stroke recovery.
OBJECTIVES
The study protocol for the SLEep Effects on Poststroke Rehabilitation (SLEEPR) Study is presented with aims of characterizing proportion of non-OSA sleep disorders in the first 90 days after stroke, evaluating the effect of non-OSA sleep disorders on poststroke recovery, and exploring the complex relationships between stroke, sleep, and recovery in the community setting.
METHODS
SLEEPR is a prospective cohort observational study across multiple study sites following individuals from inpatient rehabilitation through 90 days poststroke, with three measurement time points (inpatient rehabilitation; i.e., ~15 days poststroke, 60 days poststroke, and 90 days poststroke). Measures of sleep, function, activity, cognition, emotion, disability, and participation will be obtained for 200 people without OSA at the study's start through self-report, capacity assessments, and performance measures. Key measures of sleep include wrist actigraphy, sleep diaries, overnight oximetry, and several sleep disorders screening questionnaires (Insomnia Severity Index, Cambridge-Hopkins Restless Legs Questionnaire, Epworth Sleepiness Scale, and Sleep Disorders Screening Checklist). Key measures of function and capacity include the 10-meter walk test, Stroke Impact Scale, Barthel index, and modified Rankin scale. Key performance measures include leg accelerometry (e.g., steps/day, sedentary time, upright time, and sit-to-stand transitions) and community trips via GPS data and activity logs.
DISCUSSION
The results of this study will contribute to understanding the complex interplay between non-OSA sleep disorders and poststroke rehabilitation; they provide insight regarding barriers to participation in the community and return to normal activities after stroke. Such results could lead to strategies for developing new stroke recovery interventions.
Topics: Humans; Prospective Studies; Polysomnography; Sleep; Sleep Apnea, Obstructive; Stroke; Sleep Wake Disorders; Observational Studies as Topic
PubMed: 35948301
DOI: 10.1097/NNR.0000000000000611 -
Sensors (Basel, Switzerland) May 2023Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there... (Review)
Review
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
Topics: Humans; Respiratory Rate; Sleep; Polysomnography
PubMed: 37299762
DOI: 10.3390/s23115038 -
Internal Medicine (Tokyo, Japan) Feb 2023Objective Sleep disturbance is a common nonmotor symptom associated with a decreased quality of life in patients with Parkinson's disease (PD). In this study, we... (Clinical Trial)
Clinical Trial
Objective Sleep disturbance is a common nonmotor symptom associated with a decreased quality of life in patients with Parkinson's disease (PD). In this study, we evaluated the effects of zonisamide on motor and non-motor symptomology in patients with PD, especially with respect to objective sleep assessments conducted via polysomnography. Methods We conducted a 12-week, open-label study to assess the effects of zonisamide. The patients received 25 mg/day of zonisamide and underwent overnight polysomnography prior to and after 12 weeks of zonisamide treatment. They were assessed for their cognitive function (Mini-Mental State Examination and the Japanese version of the Montreal Cognitive Assessment), gait function (Timed Up-and-Go Test, 10-m Gait Walk Test), Parkinson's symptomology (Movement Disorder Society Revision of the Unified Parkinson's Disease Rating Scale parts 2 and 3), and self-reported sleep (Epworth Sleepiness Score, Parkinson's Disease Sleep Scale-2). Results Six patients completed the study. Polysomnographic data revealed a statistically significant increase in the percentage of time spent in sleep stage N2 (10.8%±9.2%, p=0.031) and a declining trend in the percentage of time spent in sleep stage N1 (-8.9%±12.7%, p=0.063). Although none of the patients had sleep stage N3 at baseline, 3 of the 6 patients experienced sleep stage N3 (1.1-5.4%) after 12 weeks of zonisamide treatment. The other polysomnographic parameters and clinical scores showed no statistically significant differences. Conclusions This preliminary study demonstrated that zonisamide improved objective sleep parameters measured by polysomnography in patients with PD.
Topics: Humans; Parkinson Disease; Polysomnography; Quality of Life; Sleep; Sleep Wake Disorders; Zonisamide
PubMed: 35831101
DOI: 10.2169/internalmedicine.0037-22 -
Sensors (Basel, Switzerland) Jul 2021Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals... (Review)
Review
Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.
Topics: Photoplethysmography; Polysomnography; Sleep; Sleep Stages; Wearable Electronic Devices
PubMed: 34372308
DOI: 10.3390/s21155071 -
European Archives of... Apr 2020It is not easy to assess how severe and annoying a patient's snoring is. Solid parameters are lacking; snorers cannot deliver a reliable self-assessment and it is... (Randomized Controlled Trial)
Randomized Controlled Trial
PURPOSE
It is not easy to assess how severe and annoying a patient's snoring is. Solid parameters are lacking; snorers cannot deliver a reliable self-assessment and it is uncertain whether bed partners' statements can be relied upon. The purpose of the present study was therefore to investigate whether and how well snoring assessment based on acoustic parameters and bed partners' reporting agree.
METHODS
In a double-blind, placebo-controlled study on snoring treatment, several acoustic parameters [snoring index (SI), percentage snoring time (ST), sound pressure level, sound energy, loudness, psychoacoustic annoyance and psychoacoustic snore score (PSS)] were measured in 18 subjects during 24 polysomnographies. Bed partners also assessed snoring annoyance and loudness as well as treatment outcome.
RESULTS
No correlation was found between the subjective annoyance caused by snoring and the acoustic parameters. Regarding perceived loudness, there was a moderate, significant correlation with loudness (N) and PSS over the hour with the highest SI. SI, ST, LAeq and maximum sound pressure level dB(A) showed no significant correlation. After the intervention only mean sound energy LAeq over the entire night showed a significant correlation (r = 0.782; p = 0.022) with bed partners' assessments. However, this result was not confirmed in the second control night.
CONCLUSIONS
The non-existent or only weak correlation between bed partners' ratings and objective parameters indicate that snoring severity should be evaluated with caution. Neither acoustic parameters, at least for one measurement over just one night, nor bed partners' ratings should be used as the sole basis for snoring assessment.
Topics: Acoustics; Humans; Polysomnography; Psychoacoustics; Snoring; Sound Spectrography
PubMed: 32016523
DOI: 10.1007/s00405-020-05813-2 -
Scientific Reports Jan 2021Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are...
Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
Topics: Accelerometry; Adolescent; Adult; Aged; Algorithms; Deep Learning; Female; Humans; Machine Learning; Male; Middle Aged; Polysomnography; Sleep; Sleep Stages; Sleep Wake Disorders; Wearable Electronic Devices; Young Adult
PubMed: 33420133
DOI: 10.1038/s41598-020-79217-x -
Sleep Aug 2023
Topics: Humans; Aged; Polysomnography; Reproducibility of Results; Sleep Apnea, Obstructive
PubMed: 37074871
DOI: 10.1093/sleep/zsad116