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 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.

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.

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 Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8214
Author(s):  
Suwhan Baek ◽  
Hyunsoo Yu ◽  
Jongryun Roh ◽  
Jungnyun Lee ◽  
Illsoo Sohn ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Quantitative Evaluation ◽  
Sleep Stage ◽  
Qualitative Evaluation ◽  
Parameter Analysis ◽  
Sleep Stages ◽  
Deep Sleep ◽  
Quality Of Sleep ◽  
Sleep Scoring

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

scholarly journals Sleep, Subcortical Stimulation and Kindling in the Cat

1975 ◽  
Vol 2 (4) ◽  
pp. 447-455 ◽  
Author(s):  
T. Tanaka ◽  
H. Lange ◽  
R. Naquet
Keyword(s):  
Rem Sleep ◽  
Sleep Stage ◽  
Low Frequency ◽  
Mesencephalic Reticular Formation ◽  
Seizure Threshold ◽  
Sleep Stages ◽  
Central Gray Matter ◽  
Spike Discharge ◽  
Amygdaloid Stimulation ◽  
Stimulation Of

SUMMARY:A longitudinal study of the effects of sleep on amygdaloid kindling showed that kindling disrupted normal sleep patterns by reducing REM sleep and increasing awake time. Few interictal spike discharges were observed during the awake stage, while a marked increase in discharge was observed during the light and deep sleep stages. No discharges were observed during REM sleep. During the immediate post-stimulation period the nonstimulated amygdala showed a much higher rate of spike discharge. On the other hand, there was an increase in spike discharge in the stimulated amygdala during natural sleep without preceding amygdaloid stimulation. Amygdaloid stimulation at the generalized seizure threshold during each sleep stage resulted in a generalized convulsion.The influence of subcortical electrical stimulation on kindled amygdaloid convulsions was investigated in a second experiment. Stimulation of the centre median and the caudate nucleus was without effect on kindled convulsions, while stimulation of the mesencephalic reticular formation at high frequency (300 Hz) reduced the latency of onset of kindled generalized convulsions. Stimulation of the nucleus ventralis lateralis of the thalamus at low frequency (10 Hz) prolonged the convulsion latency, and at high current levels blocked the induced convulsion. Stimulation in the central gray matter at low frequency (10 Hz) also blocked kindled amygdaloid convulsions.

scholarly journals 1193 Accuracy of a Commercial Wearable in Detecting Sleep Stages Compared to Polysomnography in Adults: Considering Sleep Classification Methods and Effects of Evening Alcohol Consumption

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A456-A457 ◽  
Author(s):  
L Menghini ◽  
V Alschuler ◽  
S Claudatos ◽  
A Goldstone ◽  
F Baker ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Cardiac Activity ◽  
Sleep Stages ◽  
Classification Methods ◽  
Management Platform ◽  
Wake Patterns ◽  
Wake Detection ◽  
Data Management Platform

Abstract Introduction Commercial wearable devices have shown the capability of collecting and processing multisensor information (motion, cardiac activity), claiming to be able to measure sleep-wake patterns and differentiate sleep stages. While using these devices, users should be aware of their accuracy, sources of measurement error and contextual factors that may affect their performance. Here, we evaluated the agreement between Fitbit Charge 2™ and PSG in adults, considering effects of two different sleep classification methods and pre-sleep alcohol consumption. Methods Laboratory-based synchronized recordings of device and PSG data were obtained from 14 healthy adults (42.6±9.7y; 6 women), who slept between one and three nights in the lab, for a total of 27 nights of data. On 10 of these nights, participants consumed alcohol (up to 4 standard drinks) in the 2 hours before bedtime. Device performance relative to PSG was evaluated using epoch-by-epoch and Bland-Altman analyses, with device data obtained from a data-management platform, Fitabase, via two methods one that accounts for short wakes (SW, awakenings that last less than 180s) and one that does not (not-SW). Results SW and not-SW methods were similar in scoring (96.76% agreement across epochs), although the SW method had better accuracy for differentiating “light”, “deep”, and REM sleep; but produced more false positives in wake detection. The device (SW-method) classified epochs of wake, “light” (N1+N2), “deep” (N3) and REM sleep with 56%, 77%, 46%, and 62% sensitivity, respectively. Bland-Altman analysis showed that the device significantly underestimated “light” (~19min) and “deep” (~26min) sleep. Alcohol consumption enhanced PSG-device discrepancies, in particular for REM sleep (p=0.01). Conclusion Our results indicate promising accuracy in sleep-wake and sleep stage identification for this device, particularly when accounting for short wakes, as compared to PSG. Alcohol consumption, as well as other potential confounders that could affect measurement accuracy should be further investigated. Support This study was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant R21-AA024841 (IMC and MdZ). The content is solely the responsibility of the authors and does not necessarily represent the official views the National Institutes of Health.

Utility of Activity Monitors and Thermometry in Assessing Sleep Stages and Sleep Quality

2018 ◽  
Vol 1 (3) ◽  
pp. 108-121
Author(s):  
Natashia Swalve ◽  
Brianna Harfmann ◽  
John Mitrzyk ◽  
Alexander H. K. Montoye
Keyword(s):  
Sleep Quality ◽  
Skin Temperature ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Activity Monitors ◽  
Research Grade

Activity monitors provide an inexpensive and convenient way to measure sleep, yet relatively few studies have been conducted to validate the use of these devices in examining measures of sleep quality or sleep stages and if other measures, such as thermometry, could inform their accuracy. The purpose of this study was to compare one research-grade and four consumer-grade activity monitors on measures of sleep quality (sleep efficiency, sleep onset latency, and wake after sleep onset) and sleep stages (awake, sleep, light, deep, REM) against an electroencephalography criterion. The use of a skin temperature device was also explored to ascertain whether skin temperature monitoring may provide additional data to increase the accuracy of sleep determination. Twenty adults stayed overnight in a sleep laboratory during which sleep was assessed using electroencephalography and compared to data concurrently collected by five activity monitors (research-grade: ActiGraph GT9X Link; consumer-grade: Fitbit Charge HR, Fitbit Flex, Jawbone UP4, Misfit Flash) and a skin temperature sensor (iButton). The majority of the consumer-grade devices overestimated total sleep time and sleep efficiency while underestimating sleep onset latency, wake after sleep onset, and number of awakenings during the night, with similar results being seen in the research-grade device. The Jawbone UP4 performed better than both the consumer- and research-grade devices, having high levels of agreement overall and in epoch-by-epoch sleep stage data. Changes in temperature were moderately correlated with sleep stages, suggesting that addition of skin temperature could increase the validity of activity monitors in sleep measurement.

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.

scholarly journals Modulation of the ultradian human nasal cycle by sleep stage and body position

2017 ◽  
Vol 75 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Richard E. Frye ◽  
Deborah F. Rosin ◽  
Adrian R. Morrison ◽  
Fidias E. Leon-Sarmiento ◽  
Richard L. Doty
Keyword(s):  
Rem Sleep ◽  
Sleep Stage ◽  
Body Position ◽  
Video Camera ◽  
Sleep Stages ◽  
Nasal Airflow ◽  
Somatosensory Stimulation ◽  
Nasal Cycle ◽  
Left And Right ◽  
The Right

ABSTRACT Objective: The nasal cycle, which is present in a significant number of people, is an ultradian side-to-side rhythm of nasal engorgement associated with cyclic autonomic activity. We studied the nasal cycle during REM/non-REM sleep stages and examined the potentially confounding influence of body position on lateralized nasal airflow. Methods: Left- and right-side nasal airflow was measured in six subjects during an eight-hour sleep period using nasal thermistors. Polysomnography was performed. Simultaneously, body positions were monitored using a video camera in conjunction with infrared lighting. Results: Significantly greater airflow occurred through the right nasal chamber (relative to the left) during periods of REM sleep than during periods of non-REM sleep (p<0.001). Both body position (p < 0.001) and sleep stage (p < 0.001) influenced nasal airflow lateralization. Conclusions: This study demonstrates that the lateralization of nasal airflow and sleep stage are related. Some types of asymmetrical somatosensory stimulation can alter this relationship.

scholarly journals Impact of sound stimulation during different sleep stages in patients with tinnitus

Loquens ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 053
Author(s):  
Marisa Pedemonte ◽  
Marcela Díaz ◽  
Eduardo Medina-Ferret ◽  
Martín Testa
Keyword(s):  
Rem Sleep ◽  
Auditory Processing ◽  
Therapeutic Strategy ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Auditory Information ◽  
Sound Stimulation ◽  
Paired Student ◽  
Before And After

It is known that auditory information is continuously processed both during wakefulness and sleep. Consistently, it has been shown that sound stimulation mimicking tinnitus during sleep decreases the intensity of tinnitus and improves the patients’ quality of life. The mechanisms underlying this effect are not known. To begin to address this question, eleven patients suffering from tinnitus were stimulated with sound mimicking tinnitus at different sleep stages; 4 were stimulated in N2, 4 in stage N3 (slow waves sleep) and 3 in REM sleep (stage with Rapid Eyes Movements). Patients’ sleep stage was monitored through polysomnography, for sound stimulation application. Tinnitus level reported by subjects were compared the days before and after stimulation and statistically analyzed (paired Student t test). All patients stimulated at stage N2 reported significantly lower tinnitus intensity the day after stimulation, while none stimulated during stage N3 and only one out of three stimulated during REM sleep showed changes. These results are consistent with studies showing that sound stimulation during N2 (sleep stage with spindles) changes power spectrum and coherence of electroencephalographic signals, and suggest that the N2 sleep stage is a critical period for reducing tinnitus intensity using this therapeutic strategy, during which auditory processing networks are more malleable by sound stimulation.

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