SCORING SLEEP: THE RULES FOR LOOKING INSIDE

2015 ◽  
Vol 14 (3) ◽  
pp. 119-124
Author(s):  
Floriana Boghez ◽  
◽  
Ioana Mandruta ◽  
Keyword(s):  
Eye Movements ◽  
Sleep Disorders ◽  
Diagnostic Tool ◽  
Video Recording ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Sleep Study ◽  
Rapid Eye Movements ◽  
Cardiac Functions ◽  
Important Diagnostic Tool

Polysomnography is the most comprehensive sleep study, a multi-parametric recording test (electroencephalography, electrooculogram, chin and limbs electromyogram, respiratory and cardiac functions and permanent video-recording), used as an important diagnostic tool for the sleep disorders. Sleep architecture or the sleep macrostructure is a term used to describe the divisions of sleep into specific sleep stages using electro encephalographic (EEG), electrooculographic (EOG) and electromyographic (EMG) criteria: NREM (non-rapid eye movements) stages – N1, N2 and N3, and REM (rapid eye movements) stage.

scholarly journals 1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A460-A461
Author(s):  
E P Pollet ◽  
D P Pollet ◽  
B Long ◽  
A A Qutub
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Sleep Study ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Large Populations

Abstract Introduction Fitness-based wearables and other emerging sensor technologies have the potential to track sleep across large populations longitudinally in at-home environments. To understand how these devices can inform research studies, limitations of available trackers need to be compared to traditional polysomnography (PSG). Here we assessed discrepancies in sleep staging in activity trackers vs. PSG in subjects with various sleep disorders. Methods Twelve subjects (age 41-78, 7f, 5m) wore a Fitbit Charge 3 while undergoing a scheduled sleep study. Six subjects had been previously diagnosed with a sleep disorder (5 OSA, 1 CSA). 4 subjects used CPAP throughout the night, 2 had a split night (CPAP 2nd half of the night), and 6 had a PSG only. Activity tracker staging was compared to 2 RPSGTs staging. Results Of the 12 subjects, eight subjects’ sleep was detected in the activity tracker, and compared across sleep stages to the PSG (7 female, 1 male, ages 41-78, AHI 0.3-87, RDI 0.5-94.4, sleep efficiency 74%+/-18, 4 PSG, 1 split, 3 CPAP). The activity tracker matched either tech 52% (+/- 13). The average difference in score tech and activity tracker staging for sleep onset (SO) was 16 +/- 15 minutes and wake after sleep onset was 43.5 +/- 44 minutes. Sensitivity, specificity, and balanced accuracy were found for each sleep stage. Respectively, Wake: 0.45+/-0.27, 0.97+/-0.03, 0.71+/-0.12, REM: 0.41+/-0.30, 0.90+/-0.06, 0.60+/-0.28, Light: 0.71+/-0.09, 0.58+/-0.19, 0.65+/-0.10, Deep: 0.63+/-0.52, 0.88+/-0.05, 0.59+/-0.49. Conclusion From this study of 12 subjects seen at a sleep clinic for suspected sleep disorders, activity trackers performed best in wake, REM and deep sleep specificity (>=88%), while they lacked sensitivity to REM and wake (<=45%) stages. The tracker did not detect sleep in 4 subjects who had elevated AHI or low sleep efficiency. Further analysis can identify whether discrepancies between the Fitbit and PSG can be predicted by distinct patterns in sleep staging and/or identify subject exclusion criteria for activity tracking studies. Support This project in on-going with the support of Academy Diagnostics Sleep and EEG Center and staff.

scholarly journals Regulation of microRNA Expression in Sleep Disorders in Patients with Epilepsy

2021 ◽  
Vol 22 (14) ◽  
pp. 7370
Author(s):  
Edyta Dziadkowiak ◽  
Justyna Chojdak-Łukasiewicz ◽  
Piotr Olejniczak ◽  
Bogusław Paradowski
Keyword(s):  
Sleep Disorders ◽  
Sleep Disorder ◽  
Diagnostic Tool ◽  
Main Role ◽  
Medical Database ◽  
Sleep Studies ◽  
Important Diagnostic Tool ◽  
Patients With Epilepsy ◽  
The Relationship

The effects of epilepsy on sleep and the activating effects of sleep on seizures are well documented in the literature. To date, many sleep-related and awake-associated epilepsy syndromes have been described. The relationship between sleep and epilepsy has led to the recognition of polysomnographic testing as an important diagnostic tool in the diagnosis of epilepsy. The authors analyzed the available medical database in search of other markers that assess correlations between epilepsy and sleep. Studies pointing to microRNAs, whose abnormal expression may be common to epilepsy and sleep disorders, are promising. In recent years, the role of microRNAs in the pathogenesis of epilepsy and sleep disorders has been increasingly emphasized. MicroRNAs are a family of single-stranded, non-coding, endogenous regulatory molecules formed from double-stranded precursors. They are typically composed of 21–23 nucleotides, and their main role involves post-transcriptional downregulation of expression of numerous genes. Learning more about the role of microRNAs in the pathogenesis of sleep disorder epilepsy may result in its use as a biomarker in these disorders and application in therapy.

751 Heart Rate and Heart Rate Variability During Sleep As Biomarkers for Depression

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A293-A293
Author(s):  
Massimiliano Grassi ◽  
Daniela Caldirola ◽  
Silvia Daccò ◽  
Giampaolo Perna ◽  
Archie Defillo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Sleep Wake Disorders ◽  
Sleep Study ◽  
Increase Risk ◽  
Patient Health ◽  
Nervous System Function ◽  
Specific Alteration

Abstract Introduction Evidence suggests a high prevalence of depression in subjects with Sleep-Wake Disorders, with impaired sleep being both a risk factor and a symptom of depression. However, depression currently remains for the most undiagnosed in this population, which can lead to a lack or delay in the treatment, and ultimately contribute to chronicity, recurrence of depression, and increase risk of suicide. Depression is characterized by alteration in sleep architecture and imbalanced autonomic nervous system function, and specific alteration may serve as biomarkers to identify ongoing depression in subjects with Sleep-Wake Disorders undergoing polysomnography. Thus, the aim of this study is to investigate differences in sleep architecture and autonomic modulation, measured by heart rate and heart rate variability throughout sleep stages, in subjects undergoing polysomnography in a sleep clinic. Methods A preliminary sample of forty subjects undergoing polysomnography was recruited in three different sleep clinics. The Patient Health Questionnaire–9 was administered to participants before the beginning of the sleep study. A cut-off of 10 was applied to identify subjects with possible current depression. The polysomnography recordings were processed with the MEBsleep software (Medibio Limited) which automatically calculats sleep architecture indices, and heart rate and heart rate variability parameters throughout sleep stages. The Mann-Whitney U test was used to investigate differences between the depressed and non-depressed groups. Results Possible current depression was found in fourteen subjects (35%). These Subjects had statistically significant higher heart rate (median depressed=78.01, median non-depressed=64.61, p=0.01) and lower Root Mean Square of the Successive Difference (RMSSD; median depressed=18.41 ms, median non-depressed=26.52 ms, p=0.02), number of pairs of successive NN intervals that differ by more than 50 ms (pNN50. Median depressed=1.62%; median non-depressed=5.64%; p=0.03), and High Frequency (absolute power) in REM (median depressed=104.17 ms2; median non-depressed=214.58 ms2; p=0.03) than those without depression. No significant differences resulted in the sleep architecture indices. Conclusion These results preliminary indicates a decreased parasympathetic activity in subjects with possible depression during REM, suggesting that heart rate and heart rate variability during sleep may be used as biomarkers to identify current depression in subjects undergoing polysomnography in sleep clinics. Support (if any):

scholarly journals The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

2019 ◽  
Author(s):  
Pierrick J. Arnal ◽  
Valentin Thorey ◽  
Michael E. Ballard ◽  
Albert Bou Hernandez ◽  
Antoine Guillot ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sleep Disorders ◽  
Large Scale ◽  
Absolute Error ◽  
Sleep Stages ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Sleep Staging ◽  
Breathing Frequency ◽  
Sleep Study

Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH’s automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm and 3.2 ± 0.6 %, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 ± 6.4% (F1 score : 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies.

Sleep Architecture and Physiology

2020 ◽  
pp. 12-22
Author(s):  
Deepa Burman ◽  
Hiren Muzumdar
Keyword(s):  
Eye Movements ◽  
Sleep Disorders ◽  
Eye Movement ◽  
Muscle Tone ◽  
Sleep Architecture ◽  
Physiological Changes ◽  
Sleep Period ◽  
Organ Systems ◽  
Normal Sleep ◽  
Normal Architecture

Sleep architecture is organized into nonrapid eye movement (Stages N1, N2, and N3) and rapid eye movement sleep that rotate through a given sleep period in discrete cycles. Each stage has specific characteristics including different electroencephalography waveforms, muscle tone, and eye movements. This chapter reviews those features and specific physiological characteristics of different stages of sleep. It also reviews how sleep architecture varies across lifespan and the importance of the knowledge of normal architecture to understand many sleep disorders. In addition, understanding physiology of normal sleep in different stages and their role in the pathophysiology of various sleep disorders are discussed. Physiological changes that occur during sleep are usually well tolerated in healthy people, but these changes may adversely affect people with vulnerable organ systems.

scholarly journals A Polysomnographic Study of Parkinson’s Disease Sleep Architecture

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Daniel Martinez-Ramirez ◽  
Sol De Jesus ◽  
Roger Walz ◽  
Amin Cervantes-Arriaga ◽  
Zhongxing Peng-Chen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Sleep Disorders ◽  
Rem Sleep ◽  
Sleep Disturbances ◽  
Sleep Stage ◽  
Rem Sleep Behavior Disorder ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Obstructive Sleep

Sleep disturbance is a common nonmotor phenomenon in Parkinson’s disease (PD) affecting patient’s quality of life. In this study, we examined the association between clinical characteristics with sleep disorders and sleep architecture patterns in a PD cohort. Patients underwent a standardized polysomnography study (PSG) in their “on medication” state. We observed that male gender and disease duration were independently associated with obstructive sleep apnea (OSA). Only lower levodopa equivalent dose (LED) was associated with periodic limb movement disorders (PLMD). REM sleep behavior disorder (RBD) was more common among older patients, with higher MDS-UPDRS III scores, and LED. None of the investigated variables were associated with the awakenings/arousals (A/A). Sleep efficiency was predicted by amantadine usage and age, while sleep stage 1 was predicted by dopamine agonists and Hoehn & Yahr severity. The use of MAO-B inhibitors and MDS-UPDRS part III were predictors of sleep stages 2 and 3. Age was the only predictor of REM sleep stage and gender for total sleep time. We conclude that sleep disorders and architecture are poorly predictable by clinical PD characteristics and other disease related factors must also be contributing to these sleep disturbances.

Wood and Bark Anatomy of Lauraceae. ii. Licaria Aublet

IAWA Journal ◽  
1985 ◽  
Vol 6 (3) ◽  
pp. 187-199 ◽  
Author(s):  
Hans Georg Richter
Keyword(s):  
Diagnostic Tool ◽  
Secondary Xylem ◽  
Secondary Phloem ◽  
Structural Profile ◽  
Quantitative Characters ◽  
Important Diagnostic Tool ◽  
Species Groups ◽  
Bark Anatomy

Qualitative features of the secondary xylem of Licaria present a rather uniform structural profile. Constant differences in primarily quantitative characters lead to the formation of speeies groups wh ich loosely correspond to infrageneric sections based on floral and vegetative morphology. This subdivision is strongly corroborated by the highly variable secondary phloem structurc revealing considerable diversity in type and distribution of sc1erenchymatic tissues. Inorganic inclusions in the secondary xylem, crystals and silica, constitute an important diagnostic tool for differentiating certain species and species groups, but are hardly of importance in the bark.

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