scholarly journals Estimating Sleep Stages Using a Head Acceleration Sensor

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 952
Author(s):  
Motoki Yoshihi ◽  
Shima Okada ◽  
Tianyi Wang ◽  
Toshihiro Kitajima ◽  
Masaaki Makikawa
Keyword(s):  
Rem Sleep ◽  
Body Movement ◽  
Sleep Stages ◽  
Sufficient Information ◽  
Deep Sleep ◽  
Head Acceleration ◽  
Adult Men ◽  
Family Issues ◽  
Leave One Out ◽  
Light Sleep

Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head.

scholarly journals Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors (Preprint)

2019 ◽  
Author(s):  
Zilu Liang ◽  
Mario Alberto Chapa-Martell
Keyword(s):  
Sleep Quality ◽  
Medical Device ◽  
Rem Sleep ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Specific Factors ◽  
Stage Transition ◽  
Light Sleep

BACKGROUND It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. OBJECTIVE This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. METHODS A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. RESULTS Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. CONCLUSIONS Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Cardiorespiratory transfer during sleep: a study in healthy young men

1995 ◽  
Vol 269 (3) ◽  
pp. H952-H958 ◽  
Author(s):  
P. Van de Borne ◽  
P. Biston ◽  
M. Paiva ◽  
H. Nguyen ◽  
P. Linkowski ◽  
...  
Keyword(s):  
Breathing Pattern ◽  
Sleep Stages ◽  
Vagal Activity ◽  
Deep Sleep ◽  
Rapid Eye Movement Sleep ◽  
Nocturnal Sleep ◽  
Healthy Men ◽  
Consistent Increase ◽  
Respiratory Modulation ◽  
Light Sleep

This study tested the concept that changes in breathing parameters account for modifications in respiratory-related blood pressure (BP) and R-R interval (RRI) variability during nocturnal sleep. BP (Finapres), electrocardiogram, respiration (Respitrace), and polygraphic sleep recordings were recorded continuously in 13 healthy men aged 18-37 yr. The transfer characteristics identified by coherence and gain measures between the calibrated thoraco-abdominal motion and the respiratory-related BP and RRI variability evidenced a consistent increase during transitions from wake to light sleep and from light to deep sleep but returned to waking levels during rapid-eye-movement sleep (P < 0.0001). These changes were related to the specific modifications occurring in the respiratory rate, tidal volume, and ribcage-to-abdominal motion ratio during the different sleep stages (0.28 < r < 0.39; P < 0.0001). This study demonstrates 1) that modifications in the breathing pattern account for 8-15% of the variance in the cardiorespiratory transfer, and 2) that respiratory modulation of vagal activity is not the main mechanism controlling the magnitude of the respiratory-related BP and RRI variability during sleep.

scholarly journals 1066 Rapid Eye Movement (REM) Sleep Duration in Posttraumatic Stress Disorder (PTSD) Varies Dynamically and Directly Within the Individual With Indices of Sleep Consolidation and Parasympathetic Tone

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A406-A406
Author(s):  
M A Gupta
Keyword(s):  
Sleep Duration ◽  
Rem Sleep ◽  
Stress Disorder ◽  
Deep Sleep ◽  
Dsm 5 ◽  
Sleep Consolidation ◽  
The Mean ◽  
Parasympathetic Tone ◽  
The Individual ◽  
Light Sleep

Abstract Introduction Autonomic arousal in posttraumatic stress disorder (PTSD) has been associated with functional hypoactivation of the medial prefrontal cortex and hyperactivity of the amygdala which can directly affect sleep physiology including REM sleep. REM sleep has been associated with reduced fear conditioning; and PTSD has been associated with REM sleep fragmentation. A case report of a drug-free PTSD patient (Gupta MA,2019) who underwent 10 home sleep apnea tests (HSATs) observed a dynamic and inverse relation between REM sleep duration and indices of sympathetic activation during sleep and sleep fragmentation. This study has examined the relationship between REM sleep duration and sleep parameters related to sleep consolidation and parasympathetic tone in 17 PTSD patients who had completed at least 10 HSATs each. Methods 17 civilian PTSD patients (all female; mean±SD age: 47.59±10.52 years; 16 white) each completed 10 HSATs (WatchPAT200, Itamar)(over 1 to 45 months). The mean±SD initial PTSD Checklist for DSM-5 score was 49.24±13.08 (n=17), and Clinician Administered PTSD Scale for DSM-5 (CAPS-5) score was ≥55. Patients using benzodiazepines and/or narcotics were excluded. Results The overall mean±SD REM duration for all 10 visits (for 17x10 HSATs) was 84.40±8.65 minutes (range 69.13-96.97 min); the mean REM duration over the 10 HSATs correlated with other sleep indices as follows: sleep onset latency (Pearson r= -0.667, p=0.035); sleep efficiency (r=0.636, p=0.048); light sleep (NI+N2) percentage (r= -0.754, p=0.012); light sleep duration (r=0.692, p=0.027);deep sleep (N3) duration (r=0.635, p=0.048). Conclusion Over the 10 HSATs the average (n=17) REM sleep duration was directly related to indices of sleep consolidation (decreased sleep latency, increased sleep efficiency, increase in both light and deep sleep duration). The direct relation of REM sleep duration to duration of deep sleep, and inverse relation with light sleep percentage suggests REM sleep- related promotion of increased parasympathetic tone within the individual. Support None

Dynamics of sleep stage transitions in healthy humans and patients with chronic fatigue syndrome

2008 ◽  
Vol 294 (6) ◽  
pp. R1980-R1987 ◽  
Author(s):  
Akifumi Kishi ◽  
Zbigniew R. Struzik ◽  
Benjamin H. Natelson ◽  
Fumiharu Togo ◽  
Yoshiharu Yamamoto
Keyword(s):  
Chronic Fatigue Syndrome ◽  
Power Law ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Chronic Fatigue ◽  
Stage I ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Healthy Humans ◽  
Fatigue Syndrome

Physiological and/or pathological implications of the dynamics of sleep stage transitions have not, to date, been investigated. We report detailed duration and transition statistics between sleep stages in healthy subjects and in others with chronic fatigue syndrome (CFS); in addition, we also compare our data with previously published results for rats. Twenty-two healthy females and 22 female patients with CFS, characterized by complaints of unrefreshing sleep, underwent one night of polysomnographic recording. We find that duration of deep sleep (stages III and IV) follows a power-law probability distribution function; in contrast, stage II sleep durations follow a stretched exponential and stage I, and REM sleep durations follow an exponential function. These stage duration distributions show a gradually increasing departure from the exponential form with increasing depth of sleep toward a power-law type distribution for deep sleep, suggesting increasing complexity of regulation of deeper sleep stages. We also find a substantial number of REM to non-REM sleep transitions in humans, while this transition is reported to be virtually nonexistent in rats. The relative frequency of this REM to non-REM sleep transition is significantly lower in CFS patients than in controls, resulting in a significantly greater relative transition frequency of moving from both REM and stage I sleep to awake. Such an alteration in the transition pattern suggests that the normal continuation of sleep in light or REM sleep is disrupted in CFS. We conclude that dynamic transition analysis of sleep stages is useful for elucidating yet-to-be-determined human sleep regulation mechanisms with pathophysiological implications.

scholarly journals Microgravity alters respiratory abdominal and rib cage motion during sleep

2009 ◽  
Vol 107 (5) ◽  
pp. 1406-1412 ◽  
Author(s):  
Rui Carlos Sá ◽  
G. Kim Prisk ◽  
Manuel Paiva
Keyword(s):  
Conformational Changes ◽  
Rem Sleep ◽  
Space Shuttle ◽  
Nrem Sleep ◽  
Deep Sleep ◽  
Tidal Breathing ◽  
Rib Cage ◽  
Leading Role ◽  
Respiratory Inductive Plethysmography ◽  
Light Sleep

The abdominal and rib cage contributions to tidal breathing differ between rapid-eye-movement (REM) and non-NREM sleep. We hypothesized that abdominal relative contribution during NREM and REM sleep would be altered in different directions when comparing sleep on Earth with sleep in sustained microgravity (μG), due to conformational changes and differences in coupling between the rib cage and the abdominal compartment induced by weightlessness. We studied respiration during sleep in five astronauts before, during, and after two Space Shuttle missions. A total of 77 full-night (8 h) polysomnographic studies were performed; abdominal and rib cage respiratory movements were recorded using respiratory inductive plethysmography. Breath-by-breath analysis of respiration was performed for each class: awake, light sleep, deep sleep, and REM sleep. Abdominal contribution to tidal breathing increased in μG, with the first measure in space being significantly higher than preflight values, followed by a return toward preflight values. This was observed for all classes. Preflight, rib cage, and abdominal movements were found to be in phase for all but REM sleep, for which an abdominal lead was observed. The abdominal leading role during REM sleep increased while deep sleep showed the opposite behavior, the rib cage taking a leading role in-flight. In μG, the percentage of inspiratory time in the overall breath, the duty cycle (TI/TTot), decreased for all classes considered when compared with preflight, while normalized inspiratory flow, taking the awake values as reference, increased in-flight for light sleep, deep sleep, and REM. Changes in abdominal-rib cage displacements probably result from a less efficient operating point for the diaphragm and a less efficient coupling between the abdomen and the apposed portion of the rib cage in μG. However, the preservation of total ventilation suggests that short-term adaptive mechanisms of ventilatory control compensate for these mechanical changes.

scholarly journals Phase discontinuities underlie increased drowsiness and diminished sleep quality in older humans

2019 ◽  
Author(s):  
Teresa Hinkle Sanders
Keyword(s):  
Sleep Quality ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Cortical Inhibition ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Older Individuals ◽  
Phase Discontinuity ◽  
Eeg Power ◽  
Smooth Transitions

AbstractHealthy humans switch seamlessly between activity states, wake up and fall asleep with regularity, and cycle through sleep stages necessary for restored homeostasis and memory consolidation each night. This study tested the hypothesis that such smooth behavioral transitions are accompanied by smooth transitions between stable neural states within the brain. A method for detecting phase discontinuities across a broad range of frequencies was created to quantify phase disruptions in the Fp-Cz EEG channel from 20 annotated sleep files. Phase discontinuities decreased with increasingly deep sleep, and increased phase discontinuity was associated with increased drowsiness, reduced deep sleep, and shorter REM sleep. A 10s phase discontinuity summary measure (the phase jump indicator) closely tracked the annotated sleep stages and enabled discrimination between short (< 10 min) and longer REM periods. Overall phase discontinuity correlated inversely with broadband EEG power, suggesting that reduced spurious signaling may facilitate increased synchronization. However, the correlation between phase discontinuity and power varied with sleep stage and age. Older individuals spent significantly more time in the Awake and Drowsy stages and less time in the deepest sleep stage and REM sleep. Interestingly, although EEG power was reduced in older individuals across all sleep stages, increased phase discontinuity only occurred in stages that showed impairment. In older patients the power vs. phase discontinuity correlation shifted to positive during drowsiness, suggesting potential deficits in cortical inhibition. These results provide evidence that phase discontinuity measures extend current capabilities for assessing sleep and may yield new insights into pathological brain states.Significance statementEvidence continues to accumulate regarding the positive relationship between healthy sleep and brain function. Recent studies also show that more healthful sleep can be induced with timely application of non-invasive therapies. Accordingly, the ability to accurately assess sleep quality in real-time has become increasingly important. Here, a newly defined measure, referred to as phase discontinuity, enabled rapid identification of unhealthful neural patterns associated with increased drowsiness, reduced deep sleep, and early termination of REM sleep. Moreover, the measure was linked to underlying neuronal and circuit properties known to impact sleep quality. Thus, the phase discontinuity measure defined in this study provides new insight into sleep pathology and has potential implications for closed-loop therapeutic intervention.

scholarly journals Characterisation of ICU sleep by a commercially available activity tracker and its agreement with patient-perceived sleep quality

2020 ◽  
Vol 7 (1) ◽  
pp. e000572 ◽  
Author(s):  
Patricia Louzon ◽  
Jessica Andrews ◽  
Xavier Torres ◽  
Eric Pyles ◽  
Mahmood Ali ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Low Cost ◽  
Sleep Time ◽  
Sleep Apnoea ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Activity Tracker ◽  
Perceived Sleep Quality

BackgroundA low-cost, quantitative method to evaluate sleep in the intensive care unit (ICU) that is both feasible for routine clinical practice and reliable does not yet exist. We characterised nocturnal ICU sleep using a commercially available activity tracker and evaluated agreement between tracker-derived sleep data and patient-perceived sleep quality.Patients and methodsA prospective cohort study was performed in a 40-bed ICU at a community teaching hospital. An activity tracker (Fitbit Charge 2) was applied for up to 7 ICU days in English-speaking adults with an anticipated ICU stay ≥2 days and without mechanical ventilation, sleep apnoea, delirium, continuous sedation, contact isolation or recent anaesthesia. The Richards-Campbell Sleep Questionnaire (RCSQ) was administered each morning by a trained investigator.ResultsAvailable activity tracker-derived data for each ICU study night (20:00–09:00) (total sleep time (TST), number of awakenings (#AW), and time spent light sleep, deep sleep and rapid eye movement (REM) sleep) were downloaded and analysed. Across the 232 evaluated nights (76 patients), TST and RCSQ data were available for 232 (100%), #AW data for 180 (78%) and sleep stage data for 73 (31%). Agreement between TST (349±168 min) and RCSQ Score was moderate and significant (r=0.34; 95% CI 0.18 to 0.48). Agreement between #AW (median (IQR), 4 (2–9)) and RCSQ Score was negative and non-significant (r=−0.01; 95% CI −0.19 to 0.14). Agreement between time (min) spent in light (259 (182 to 328)), deep (43±29), and REM (47 (28–72)) sleep and RCSQ Score was moderate but non-significant (light (r=0.44, 95% CI −0.05 to 0.36); deep sleep (r=0.44, 95% CI −0.11 to 0.15) and REM sleep (r=0.44; 95% CI −0.21 to 0.21)).ConclusionsA Fitbit Charge 2 when applied to non-intubated adults in an ICU consistently collects TST data but not #AW or sleep stage data at night. The TST moderately correlates with patient-perceived sleep quality; a correlation between either #AW or sleep stages and sleep quality was not found.

scholarly journals Sex differences in sleep and influence of the menstrual cycle on women’s sleep in junior endurance athletes

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253376
Author(s):  
Maria Hrozanova ◽  
Christian A. Klöckner ◽  
Øyvind Sandbakk ◽  
Ståle Pallesen ◽  
Frode Moen
Keyword(s):  
Sex Differences ◽  
Menstrual Cycle ◽  
Female Athletes ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Rem Latency ◽  
Light Sleep

Previous research shows that female athletes sleep better according to objective parameters but report worse subjective sleep quality than male athletes. However, existing sleep studies did not investigate variations in sleep and sleep stages over longer periods and have, so far, not elucidated the role of the menstrual cycle in female athletes’ sleep. To address these methodological shortcomings, we investigated sex differences in sleep and sleep stages over 61 continuous days in 37 men and 19 women and examined the role of the menstrual cycle and its phases in 15 women. Sleep was measured by a non-contact radar, and menstrual bleeding was self-reported. Associations were investigated with multilevel modeling. Overall, women tended to report poorer subjective sleep quality (p = .057), but objective measurements showed that women obtained longer sleep duration (p < .001), more light (p = .013) and rapid eye movement sleep (REM; hours (h): p < .001, %: p = .007), shorter REM latency (p < .001), and higher sleep efficiency (p = .003) than men. R2 values showed that sleep duration, REM and REM latency were especially affected by sex. Among women, we found longer time in bed (p = .027) and deep sleep (h: p = .036), and shorter light sleep (%: p = .021) during menstrual bleeding vs. non-bleeding days; less light sleep (h: p = .040), deep sleep (%: p = .013) and shorter REM latency (p = .011) during the menstrual than pre-menstrual phase; and lower sleep efficiency (p = .042) and more deep sleep (%: p = .026) during the follicular than luteal phase. These findings indicate that the menstrual cycle may impact the need for physiological recovery, as evidenced by the sleep stage variations. Altogether, the observed sex differences in subjective and objective sleep parameters may be related to the female athletes’ menstrual cycle. The paper provides unique data of sex differences in sleep stages and novel insights into the role of the menstrual cycle in sleep among female athletes.

scholarly journals 193 Sleep Variability and Affect Dynamics Among College Students during COVID-19 Pandemic

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A78-A78
Author(s):  
Zahra Mousavi ◽  
Jocelyn Lai ◽  
Asal Yunusova ◽  
Alexander Rivera ◽  
Sirui Hu ◽  
...  
Keyword(s):  
Emerging Adults ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Positive And Negative Affect ◽  
Sleep Onset Latency ◽  
Deep Sleep ◽  
Onset Latency ◽  
Sleep Patterns ◽  
Light Sleep ◽  
Affect Dynamics

Abstract Introduction Sleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently mood-related disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic. Methods College student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state. Results Multilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p&lt;.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps&lt; .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity). Conclusion Sleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities. Support (if any):

scholarly journals Cerebral functional networks during sleep in young and older individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Véronique Daneault ◽  
Pierre Orban ◽  
Nicolas Martin ◽  
Christian Dansereau ◽  
Jonathan Godbout ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Plasticity ◽  
Network Connectivity ◽  
Sleep Stages ◽  
Functional Networks ◽  
Older Individuals ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Age Related ◽  
Light Sleep

AbstractEven though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

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