-
The Nurse Practitioner Mar 2022Patients with insomnia have been shown to use many maladaptive coping mechanisms. This article examines the effect of such coping mechanisms on sleep quality by...
Patients with insomnia have been shown to use many maladaptive coping mechanisms. This article examines the effect of such coping mechanisms on sleep quality by reviewing results of polysomnography and the Epworth Sleepiness Scale among 137 patients. The study showed that sleep quality was affected by these mechanisms.
Topics: Adaptation, Psychological; Humans; Polysomnography; Sleep; Sleep Initiation and Maintenance Disorders; Surveys and Questionnaires
PubMed: 35171865
DOI: 10.1097/01.NPR.0000819624.10897.33 -
Arquivos de Neuro-psiquiatria Oct 2020
Topics: Humans; Polysomnography; REM Sleep Behavior Disorder; Sleep, REM
PubMed: 33146234
DOI: 10.1590/0004-282X20200189 -
Sleep Medicine Clinics Sep 2017This article describes clinical approaches to assessing sleepiness. Subjective sleep scales are used in clinical settings but have significant limitations. Likewise,... (Review)
Review
This article describes clinical approaches to assessing sleepiness. Subjective sleep scales are used in clinical settings but have significant limitations. Likewise, objective tools may have prohibitive expense, and practical administration considerations may prohibit regular use. Gold standard tests include the multiple sleep latency test and maintenance of wakefulness test. These studies are criticized for a variety of reasons but are useful in appropriate clinical context. New tools suggest novel ways to assess sleepiness and will likely be more prominent in clinical assessments over time. This article outlines subjective scales and objective tools and suggests situations where particular instruments may be appropriate.
Topics: Diagnostic Self Evaluation; Disorders of Excessive Somnolence; Humans; Polysomnography; Psychometrics
PubMed: 28778230
DOI: 10.1016/j.jsmc.2017.03.007 -
Epilepsia Open Sep 2023This study evaluated sleep and respiratory abnormalities, and their relationship with seizures, in adults with developmental and epileptic encephalopathies (DEEs). We...
This study evaluated sleep and respiratory abnormalities, and their relationship with seizures, in adults with developmental and epileptic encephalopathies (DEEs). We studied consecutive adults with DEEs undergoing inpatient video-EEG monitoring and concurrent polysomnography between December 2011 and July 2022. Thirteen patients with DEEs were included (median age: 31 years, range: 20-50; 69.2% female): Lennox-Gastaut syndrome (n = 6), Lennox-Gastaut syndrome-like phenotype (n = 2), Landau-Kleffner syndrome (n = 1), epilepsy with myoclonic-atonic seizures (n = 1), and unclassified DEEs (n = 3). Sleep architecture was often fragmented by epileptiform discharges and seizures resulting in arousals (median arousal index: 29.0 per h, range: 5.1-65.3). Moderate-to-severe obstructive sleep apnea (OSA) was observed in seven patients (53.8%). Three patients (23.1%) had tonic seizures that frequently occurred with central apnea; one met criteria for mild central sleep apnea. Of the patients with tonic seizures, two had other identifiable seizure manifestations, but in one patient, central apnea was commonly the only discernable seizure manifestation. Polysomnography during video-EEG is an effective diagnostic tool in detecting sleep and seizure-related respiratory abnormalities. Clinically significant OSA may increase the risk of comorbid cardiovascular disease and premature mortality. Treatment of epilepsy may improve sleep quality, and conversely, improved sleep, may decrease seizure burden.
Topics: Female; Male; Humans; Polysomnography; Lennox Gastaut Syndrome; Sleep Apnea, Central; Sleep; Seizures; Electroencephalography; Sleep Apnea, Obstructive
PubMed: 37277988
DOI: 10.1002/epi4.12772 -
Otolaryngology--head and Neck Surgery :... May 2023To provide an updated comparison of apnea-hypopnea index (AHI), oxygen desaturation index (ODI), respiratory disturbance index (RDI), oxygen saturation (O sat), and... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
To provide an updated comparison of apnea-hypopnea index (AHI), oxygen desaturation index (ODI), respiratory disturbance index (RDI), oxygen saturation (O sat), and lowest oxyhemoglobin saturation (LSAT) measured by portable sleep study devices (PSSDs) compared to polysomnography (PSG).
DATA SOURCES
Primary studies were identified through PubMed, Scopus, CINAHL, and Cochrane.
REVIEW METHODS
A systematic review was performed by searching databases from inception through August 2021. Only studies examining simultaneous monitoring of a PSSD and PSG were included. Respiratory indices AHI, ODI, RDI, O sat, and LSAT was collected Meta-correlations and meta-regressions were conducted to compare sleep variable measurements between PSSD and PSG.
RESULTS
A total of 24 studies (N = 1644 patients) were included. The mean age was 49.5 ± 12.0 (range = 13-92), mean body mass index (BMI) was 30.4 ± 5.7 (range = 17-87), and 69.4% were male. Meta-correlation showed significant associations between PSSD and PSG for AHI (n = 655, r = .888; p < .001), ODI (n = 241, r = .942; p < .001), RDI (n = 313, r = .832; p < .001), O sat (n = 171, r = .858; p < .001), and LSAT (n = 197, r = .930; p < .001). Meta-regressions indicated significant predictive correlations for AHI (n = 655; r = .96; p < .001), ODI (n = 740; r = .75; p = .031), RDI (n = 197; r = .99; p = .005), and LSAT (n = 197; r = .85; p = .030), but not for O sat (n = 171; r = .31; p = .692).
CONCLUSIONS
Respiratory indices correlate strongly between PSSD and PSG, which is further supported by meta-regressions results. PSSD might be a valuable cost and time-saving OSA screening tool.
Topics: Humans; Male; Adult; Middle Aged; Female; Polysomnography; Sleep Apnea, Obstructive; Oxygen; Body Mass Index; Sleep
PubMed: 36939562
DOI: 10.1002/ohn.179 -
American Family Physician Feb 2022Childhood sleep disorders can disrupt family dynamics and cause cognitive and behavior problems. Early recognition and management can prevent these complications....
Childhood sleep disorders can disrupt family dynamics and cause cognitive and behavior problems. Early recognition and management can prevent these complications. Behavior subtypes of childhood insomnias affect 10% to 30% of children and result from inconsistent parental limit-setting and improper sleep-onset association. Behavior insomnias are treated using extinction techniques and parent education. Hypnotic medications are not recommended. Obstructive sleep apnea affects 1% to 5% of children. Polysomnography is required to diagnose obstructive sleep apnea; history and physical examination alone are not adequate. Adenotonsillectomy is the first-line treatment for obstructive sleep apnea. Nasal continuous positive airway pressure is the second-line treatment for children who do not respond to surgery or if adenotonsillectomy is contraindicated. Restless legs syndrome can be difficult to recognize and has an association with attention-deficit/hyperactivity disorder. Management of restless legs syndrome includes treatment of iron deficiency, if identified, and removal of triggering factors. Parasomnias affect up to 50% of children and usually resolve spontaneously by adolescence. Management of parasomnias involves parental education, reassurance, safety precautions, and treating comorbid conditions. Delayed sleep phase syndrome is found during adolescence, manifesting as a night owl preference. Treatment of delayed sleep phase syndrome includes sleep hygiene, nighttime melatonin, and morning bright light exposure. Sleep deprivation is of increasing concern, affecting 68% of people in high school.
Topics: Continuous Positive Airway Pressure; Humans; Parasomnias; Polysomnography; Sleep Apnea, Obstructive; Sleep Wake Disorders
PubMed: 35166510
DOI: No ID Found -
Sleep Medicine Clinics Dec 2016Currently, 2 sets of similar rules for recording and scoring leg movement (LM) exist, including periodic LM during sleep (PLMS) and periodic LM during wakefulness. The... (Review)
Review
Currently, 2 sets of similar rules for recording and scoring leg movement (LM) exist, including periodic LM during sleep (PLMS) and periodic LM during wakefulness. The former were published in 2006 by a task force of the International Restless Legs Syndrome Study Group, and the second in 2007 by the American Academy of Sleep Medicine. This article reviews the basic recording methods, scoring rules, and computer-based programs for PLMS. Less frequent LM activities, such as alternating leg muscle activation, hypnagogic foot tremor, high-frequency LMs, and excessive fragmentary myoclonus are briefly described.
Topics: Extremities; Humans; Movement; Polysomnography; Sleep
PubMed: 28118866
DOI: 10.1016/j.jsmc.2016.08.005 -
Annals of Biomedical Engineering Jun 2024In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on... (Review)
Review
In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.
Topics: Humans; Sleep; Machine Learning; Polysomnography; Electroencephalography; Signal Processing, Computer-Assisted; Support Vector Machine
PubMed: 38493234
DOI: 10.1007/s10439-024-03486-0 -
Journal of the American Board of Family... 2015Obstructive sleep apnea (OSA) is a fairly common condition that, if left untreated, can lead to complications such as high blood pressure and heart disease.... (Review)
Review
Obstructive sleep apnea (OSA) is a fairly common condition that, if left untreated, can lead to complications such as high blood pressure and heart disease. Polysomnography (PSG) is the most accurate method for diagnosing OSA, but it is a cumbersome and expensive test. A well-validated, easier to perform and less expensive alternative is the home sleep test (HST). The purpose of this review is to educate the primary care provider about the important differences between PSG and HSTs, the advantages and limitations of both modalities, identifying patients who are appropriate candidates for the HST, identifying patients in whom the HST should not be performed, and further evaluation of patients who have a negative HST.
Topics: Algorithms; Clinical Decision-Making; Decision Support Techniques; Humans; Polysomnography; Primary Health Care; Sleep Apnea, Obstructive
PubMed: 26152443
DOI: 10.3122/jabfm.2015.04.140266 -
Scientific Reports May 2022The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can...
The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04-0.06 and a total sleep time (TST) deviation of [Formula: see text]2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between [Formula: see text]29.07 and [Formula: see text]55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.
Topics: Heart Rate; Humans; Polysomnography; Reproducibility of Results; Sleep; Wearable Electronic Devices
PubMed: 35562527
DOI: 10.1038/s41598-022-11792-7