Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers

2017 ◽  
Vol 16 (04) ◽  
pp. 1750033 ◽  
Author(s):  
Martin O. Mendez ◽  
Elvia R. Palacios-Hernandez ◽  
Alfonso Alba ◽  
Juha M. Kortelainen ◽  
Mirja L. Tenhunen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Performance Measures ◽  
Night Shift ◽  
Sleep Stages ◽  
Support Vector ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Kappa Index ◽  
Extensive Evaluation ◽  
Night And Day

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

Detrended Fluctuation Analysis Features for Automated Sleep Staging of Sleep EEG

2012 ◽  
Vol 1 (4) ◽  
pp. 47-59 ◽  
Author(s):  
Amr F. Farag ◽  
Shereen M. El-Metwally
Keyword(s):  
Eye Movement ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Fluctuation Analysis ◽  
Eeg Signal ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Detrended Fluctuation

An accurate sleep staging is crucial for the treatment of sleep disorders. Recently some studies demonstrated that the long range correlations of many physiological signals measured during sleep show some variations during the different sleep stages. In this study, detrended fluctuation analysis (DFA) is used to study the electroencephalogram (EEG) signal autocorrelation during different sleep stages. A classification of these stages is then made by introducing the calculated DFA power law exponents to a K-Nearest Neighbor classifier. The authors’ study reveals that a 2-D feature space composed of the DFA power law exponents of both the filtered THETA and BETA brain waves resulted in a classification accuracy of 93.52%, 93.52%, and 92.59% for the wake, non-rapid eye movement and rapid eye movement stages, respectively. The overall accuracy of the proposed system is 93.21%. The authors conclude that it might be possible to build an automated sleep assessment system based on DFA analysis of the sleep EEG signal.

High-throughput visual assessment of sleep stages in mice using machine learning

SLEEP ◽  
2021 ◽  
Author(s):  
Brian Geuther ◽  
Mandy Chen ◽  
Raymond J Galante ◽  
Owen Han ◽  
Jie Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
High Throughput ◽  
Data Augmentation ◽  
Sleep Stage ◽  
Visual Assessment ◽  
Video Data ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Abstract Study Objectives Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice. Methods We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods. Results Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration. Conclusions We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Abstract P417: Relationship Between Blood Pressure Variability and Sleep Architecture

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Xiaoyue Liu ◽  
Jeongok G Logan ◽  
Younghoon Kwon ◽  
Jennifer Lobo ◽  
Hyojung Kang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Eye Movement ◽  
Rem Sleep ◽  
Sleep Time ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Apnea Hypopnea Index ◽  
Rapid Eye Movement ◽  
Sleep Studies ◽  
The Relationship

Introduction: Blood pressure (BP) variability (BPV) is a novel marker for cardiovascular disease (CVD) independent of high BP. Sleep architecture represents the structured pattern of sleep stages consisting of rapid eye movement (REM) and non-rapid eye movement (NREM), and it is an important element in the homeostatic regulation of sleep. Currently, little is known regarding whether BPV is linked to sleep stages. Our study aimed to examine the relationship between sleep architecture and BPV. Methods: We analyzed in-lab polysomnographic studies collected from individuals who underwent diagnostic sleep studies at a university hospital from 2010 to 2017. BP measures obtained during one year prior to the sleep studies were included. BPV was computed using the coefficient of variation for all individuals who had three or more systolic and diastolic BP data. We conducted linear regression analysis to assess the relationship of systolic BPV (SBPV) and diastolic BPV (DBPV) with the sleep stage distribution (REM and NREM sleep time), respectively. Covariates that can potentially confound the relationships were adjusted in the models, including age, sex, race/ethnicity, body mass index, total sleep time, apnea-hypopnea index, mean BP, and history of medication use (antipsychotics, antidepressants, and antihypertensives) during the past two years before the sleep studies. Results: Our sample (N=3,565; male = 1,353) was racially and ethnically diverse, with a mean age 54 ± 15 years and a mean BP of 131/76 ± 13.9/8.4 mmHg. Among the sleep architecture measures examined, SBPV showed an inverse relationship with REM sleep time after controlling for all covariates ( p = .033). We subsequently categorized SBPV into four quartiles and found that the 3 rd quartile (mean SBP SD = 14.9 ± 2.1 mmHg) had 3.3 fewer minutes in REM sleep compared to the 1 st quartile ( p = .02). However, we did not observe any relationship between DBPV and sleep architecture. Conclusion: Greater SBPV was associated with lower REM sleep time. This finding suggests a possible interplay between BPV and sleep architecture. Future investigation is warranted to clarify the directionality, mechanism, and therapeutic implications.

Fast-response whole body indirect calorimeters for infants

1993 ◽  
Vol 74 (1) ◽  
pp. 476-484 ◽  
Author(s):  
J. K. Moon ◽  
C. L. Jensen ◽  
N. F. Butte
Keyword(s):  
Eye Movement ◽  
Fast Response ◽  
Full Term ◽  
Whole Body ◽  
Co2 Production ◽  
Sleep Stages ◽  
System Response ◽  
O2 Consumption ◽  
Rapid Eye Movement ◽  
Term Infants

Portable whole body indirect calorimeters were constructed for full-term (2.5- to 8-kg) and preterm (1- to 2.5-kg) infants. A new calibration system significantly increased the accuracy of flowmeters and gas analyzers. Performance tests with N2 and CO2 infusions and butane combustion demonstrated that the error of individual measurements of O2 consumption and CO2 production were within +/- 2%. The measured error was close to the theoretical uncertainty of approximately +/- 1% calculated from test results of the flowmeters and gas analyzers. System response to a step change in butane combustion rate exceeded 90% within 2 min. Error of +/- 2% and response of 2 min are likely to be the practical lower limits for whole body infant indirect calorimeters with current technology. The calorimeters demonstrated a rapid increase in O2 consumption after feeding (preterm infants) and in the transition from non-rapid-eye-movement to rapid-eye-movement sleep stages (full-term infants).

Influence of sleep on tensor palatini EMG and upper airway resistance in normal men

1991 ◽  
Vol 70 (6) ◽  
pp. 2574-2581 ◽  
Author(s):  
D. J. Tangel ◽  
W. S. Mezzanotte ◽  
D. P. White
Keyword(s):  
Eye Movement ◽  
Airway Resistance ◽  
Sleep Stage ◽  
Upper Airway ◽  
Sleep Stages ◽  
Normal Male ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Airway Narrowing ◽  
Upper Airway Resistance

We propose that a sleep-induced decrement in the activity of the tensor palatini (TP) muscle could induce airway narrowing in the area posterior to the soft palate and therefore lead to an increase in upper airway resistance in normal subjects. We investigated the TP to determine the influence of sleep on TP muscle activity and the relationship between changing TP activity and upper airway resistance over the entire night and during short sleep-awake transitions. Seven normal male subjects were studied on a single night with wire electrodes placed in both TP muscles. Sleep stage, inspiratory airflow, transpalatal pressure, and TP moving time average electromyogram (EMG) were continuously recorded. In addition, in two of the seven subjects the activity (EMG) of both the TP and the genioglossus muscle simultaneously was recorded throughout the night. Upper airway resistance increased progressively from wakefulness through the various non-rapid-eye-movement sleep stages, as has been previously described. The TP EMG did not commonly demonstrate phasic activity during wakefulness or sleep. However, the tonic EMG decreased progressively and significantly (P less than 0.05) from wakefulness through the non-rapid-eye-movement sleep stages [awake, 4.6 +/- 0.3 (SE) arbitrary units; stage 1, 2.6 +/- 0.3; stage 2, 1.7 +/- 0.5; stage 3/4, 1.5 +/- 0.8]. The mean correlation coefficient between TP EMG and upper airway resistance across all sleep states was (-0.46). This mean correlation improved over discrete sleep-awake transitions (-0.76). No sleep-induced decrement in the genioglossus activity was observed in the two subjects studied.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals Effects of sleep on the cardiovascular and thermoregulatory systems: a possible role for hypocretins

2010 ◽  
Vol 109 (4) ◽  
pp. 1053-1063 ◽  
Author(s):  
H. Schwimmer ◽  
H. M. Stauss ◽  
F. Abboud ◽  
S. Nishino ◽  
E. Mignot ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Eye Movement ◽  
Time Of Day ◽  
Male Rats ◽  
State Transitions ◽  
Sleep Stages ◽  
Electromyographic Activity ◽  
Rapid Eye Movement ◽  
Hypocretin Neuron

Sleep influences the cardiovascular, endocrine, and thermoregulatory systems. Each of these systems may be affected by the activity of hypocretin (orexin)-producing neurons, which are involved in the etiology of narcolepsy. We examined sleep in male rats, either hypocretin neuron-ablated orexin/ataxin-3 transgenic (narcoleptic) rats or their wild-type littermates. We simultaneously monitored electroencephalographic and electromyographic activity, core body temperature, tail temperature, blood pressure, electrocardiographic activity, and locomotion. We analyzed the daily patterns of these variables, parsing sleep and circadian components and changes between states of sleep. We also analyzed the baroreceptor reflex. Our results show that while core temperature and heart rate are affected by both sleep and time of day, blood pressure is mostly affected by sleep. As expected, we found that both blood pressure and heart rate were acutely affected by sleep state transitions in both genotypes. Interestingly, hypocretin neuron-ablated rats have significantly lower systolic and diastolic blood pressure during all sleep stages (non-rapid eye movement, rapid eye movement) and while awake (quiet, active). Thus, while hypocretins are critical for the normal temporal structure of sleep and wakefulness, they also appear to be important in regulating baseline blood pressure and possibly in modulating the effects of sleep on blood pressure.

Effects of exercise on sleep

1978 ◽  
Vol 44 (6) ◽  
pp. 945-951 ◽  
Author(s):  
J. M. Walker ◽  
T. C. Floyd ◽  
G. Fein ◽  
C. Cavness ◽  
R. Lualhati ◽  
...  
Keyword(s):  
Eye Movement ◽  
Slow Wave Sleep ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
Absolute Amount ◽  
Rapid Eye Movement ◽  
Personality Profiles ◽  
Experimental Conditions ◽  
Strong Trend ◽  
Delta Activity

We tested the hypothesis that EEG sleep stages 3 and 4 (slow-wave sleep, SWS) would be increased as a function of either acute of chronic exercise. Ten distance runners were matched with 10 nonrunners, and their sleep was recorded under both habitual (runners running and nonrunners not running, 3 night) and abruptly changed (runners not running and nonrunners running, 1 night) conditions. Analyses of both visually scored SWS and computer measures of delta activity during non-rapid eye-movement (NREM) sleep failed to support the SWS-exercise hypothesis. The runners showed a significantly higher proportion and a greater absolute amount of NREM sleep than the nonrunners. The runners showed less rapid eye-movement activity during sleep than the nonrunners under both experimental conditions, indicating a strong and unexpected effect of physical fitness on this measure. Modest afternoon exercise in nonrunners was associated with a strong trend toward elevated heart rate during sleep. Mood tests and personality profiles revealed few differences, either between groups or within groups, as a function of exercise.

Metabolic rate and breathing during sleep

1985 ◽  
Vol 59 (2) ◽  
pp. 384-391 ◽  
Author(s):  
D. P. White ◽  
J. V. Weil ◽  
C. W. Zwillich
Keyword(s):  
Metabolic Rate ◽  
Eye Movement ◽  
Sleep Stage ◽  
Minute Ventilation ◽  
Respiratory Control ◽  
Normal Subjects ◽  
Co2 Production ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Recent investigation suggests that both ventilation (VE) and the chemical sensitivity of the respiratory control system correlate closely with measures of metabolic rate [O2 consumption (VO2) and CO2 production (VCO2)]. However, these associations have not been carefully investigated during sleep, and what little information is available suggests a deterioration of the relationships. As a result we measured VE, ventilatory pattern, VO2, and VCO2 during sleep in 21 normal subjects (11 males and 10 females) between the ages of 21 and 77 yr. When compared with values for awake subjects, expired ventilation decreased 8.2 +/- 2.3% (SE) during sleep and was associated with a 8.5 +/- 1.6% decrement in VO2 and a 12.3 +/- 1.7% reduction in VCO2, all P less than 0.01. The decrease in ventilation was a product primarily of a significant decrease in tidal volume with little change in frequency. None of these findings were dependent on sleep stage with results in rapid-eye-movement (REM) and non-rapid-eye-movement sleep being similar. Through all sleep stages ventilation remained tightly correlated with VO2 and VCO2 both within a given individual and between subjects. Although respiratory rhythmicity was somewhat variable during REM sleep, minute ventilation continued to correlate with VO2 and VCO2. None of the parameters described above were influenced by age or gender, with male and female subjects demonstrating similar findings. Ten of the subjects demonstrated at least occasional apneas. These individuals, however, were not found to differ from those without apnea in any other measure of ventilation or metabolic rate.

scholarly journals Why Are Seizures Rare in Rapid Eye Movement Sleep? Review of the Frequency of Seizures in Different Sleep Stages

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Marcus Ng ◽  
Milena Pavlova
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Low Voltage ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Seizure Freedom ◽  
Rapid Eye Movement ◽  
Focal Seizures ◽  
Generalized Seizures ◽  
Interictal Discharges

Since the formal characterization of sleep stages, there have been reports that seizures may preferentially occur in certain phases of sleep. Through ascending cholinergic connections from the brainstem, rapid eye movement (REM) sleep is physiologically characterized by low voltage fast activity on the electroencephalogram, REMs, and muscle atonia. Multiple independent studies confirm that, in REM sleep, there is a strikingly low proportion of seizures (~1% or less). We review a total of 42 distinct conventional and intracranial studies in the literature which comprised a net of 1458 patients. Indexed to duration, we found that REM sleep was the most protective stage of sleep against focal seizures, generalized seizures, focal interictal discharges, and two particular epilepsy syndromes. REM sleep had an additional protective effect compared to wakefulness with an average 7.83 times fewer focal seizures, 3.25 times fewer generalized seizures, and 1.11 times fewer focal interictal discharges. In further studies REM sleep has also demonstrated utility in localizing epileptogenic foci with potential translation into postsurgical seizure freedom. Based on emerging connectivity data in sleep, we hypothesize that the influence of REM sleep on seizures is due to a desynchronized EEG pattern which reflects important connectivity differences unique to this sleep stage.

Partitioning of inhaled ventilation between the nasal and oral routes during sleep in normal subjects

2003 ◽  
Vol 94 (3) ◽  
pp. 883-890 ◽  
Author(s):  
Michael F. Fitzpatrick ◽  
Helen S. Driver ◽  
Neela Chatha ◽  
Nha Voduc ◽  
Alison M. Girard
Keyword(s):  
Eye Movement ◽  
Slow Wave ◽  
Sleep Stage ◽  
Minute Ventilation ◽  
Normal Subjects ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Nasal Mask ◽  
Rapid Eye Movement Sleep ◽  
Significant Difference

The oral and nasal contributions to inhaled ventilation were simultaneously quantified during sleep in 10 healthy subjects (5 men, 5 women) aged 43 ± 5 yr, with normal nasal resistance (mean 2.0 ± 0.3 cmH2O · l−1 · s−1) by use of a divided oral and nasal mask. Minute ventilation awake (5.9 ± 0.3 l/min) was higher than that during sleep (5.2 ± 0.3 l/min; P < 0.0001), but there was no significant difference in minute ventilation between different sleep stages ( P = 0.44): stage 2 5.3 ± 0.3, slow-wave 5.2 ± 0.2, and rapid-eye-movement sleep 5.2 ± 0.2 l/min. The oral fraction of inhaled ventilation during wakefulness (7.6 ± 4%) was not significantly different from that during sleep (4.3 ± 2%; mean difference 3.3%, 95% confidence interval −2.1–8.8%, P = 0.19), and no significant difference ( P = 0.14) in oral fraction was observed between different sleep stages: stage two 5.1 ± 2.8, slow-wave 4.2 ± 1.8, rapid-eye-movement 3.1 ± 1.7%. Thus the inhaled oral fraction in normal subjects is small and does not change significantly with sleep stage.

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