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 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 0746 Dynamics of Sleep Stage Transitions in Patients with Narcolepsy and Other Hypersomnias

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A283-A284
Author(s):  
A Kishi ◽  
T Kitajima ◽  
R Kawai ◽  
M Hirose ◽  
N Iwata ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Stages ◽  
Subsequent Stage ◽  
Dynamic Features ◽  
Survival Curves ◽  
Transition Analysis ◽  
Sleep Transition

Abstract Introduction Narcolepsy is a chronic sleep disorder characterized by excessive daytime sleepiness and abnormal REM sleep phenomena. Narcolepsy can be distinguished into type 1 (NT1; with cataplexy) and type 2 (NT2; without cataplexy). It has been reported that sleep stage sequences at sleep-onset as well as sleep-wake dynamics across the night may be useful in the differential diagnosis of hypersomnia. Here we studied dynamic features of sleep stage transitions during whole night sleep in patients with NT1, NT2, and other types of hypersomnia (o-HS). Methods Twenty patients with NT1, 14 patients with NT2, and 35 patients with o-HS underwent overnight PSG. Transition probabilities between sleep stages (wake, N1, N2, N3, and REM) and survival curves of continuous runs of each sleep stage were compared between groups. Transition-specific survival curves of continuous runs of each sleep stage, dependent on the subsequent stage of the transition, were also compared. Results The probability of transitions from N1-to-wake was significantly greater in NT1 than in NT2 and o-HS while that from N1-to-N2 was significantly smaller in NT1 than in NT2 and o-HS. The probability of transitions from N2-to-REM was significantly smaller in NT1 than in o-HS. Wake and N1 were significantly more continuous in NT1 than in NT2; specifically, N1 followed by N2 was significantly more continuous in NT1 than in NT2 and o-HS. N2 was significantly less continuous in NT1 and NT2 than in o-HS; this was specifically confirmed for N2 followed by N1/wake. REM sleep was significantly less continuous in NT1 than in NT2 and o-HS; specifically, REM sleep followed by wake was significantly less continuous in NT1 than in o-HS. Continuity of N3 did not differ significantly between groups. Conclusion Dynamics of sleep stage transitions differed between NT1, NT2, and o-HS. Dynamic features of sleep such as sleep instability, persistency of wake/N1, and REM fragmentation may differentiate NT1 from NT2, while N2 continuity may differentiate narcolepsy from o-HS. The results suggest that sleep transition analysis may be of clinical utility and provide insights into the underlying pathophysiology of hypersomnia and narcolepsy. Support JSPS KAKENHI (18K17891 to AK).

Sleep Stage Transitions in Healthy Humans Altered by Central Monoaminergic Antagonist

2010 ◽  
Vol 49 (05) ◽  
pp. 458-461 ◽  
Author(s):  
A. Kishi ◽  
H. Yasuda ◽  
T. Matsumoto ◽  
Y. Inami ◽  
J. Horiguchi ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Young Male ◽  
Stage Ii ◽  
Stage Iii ◽  
Sleep Stages ◽  
Healthy Humans ◽  
Sleep Rhythms ◽  
Dynamical Aspect

Summary Objectives: Sleep stage transitions constitute one of the key components of the dynamical aspect of sleep. However, neural mechanisms of sleep stage transitions have not, to date, been fully elucidated. We investigate the effects of administrating risperi-done, a central serotonergic and dopaminergic antagonist, on sleep stage transitions inhumans, and also on ultradian rapid-eye-movement (REM) sleep rhythms. Methods: Ten healthy young male volunteers (age: 22 ± 3.7 years) participated in this study. The subjects spent three nights in a sleep laboratory. The first was the adaptation night, and the second was the baseline night. On the third night, the subjects received risperidone (1 mg tablet) 30 min before the polysomnography recording. We measured and investigated transition probabilities between waking, REM and non-REM (stages I–IV) sleep stages. Results: We found that the probability of transition from stage II to stage III was significantly greater for the risperidone night than for the baseline night. We also found that risperidone administration prolonged REM-onset intervals, when compared to the baseline night. Conclusions: We demonstrate that central serotonergic and /or dopaminergic neural transmissions are involved in the regulation of sleep stage transitions from light (stage II) to deep (stage III) sleep, and also in determining ultradian REM sleep rhythms.

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 Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability

2020 ◽  
Author(s):  
Hongbo Ni ◽  
Mingzhe GUO ◽  
Ying Wang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Transition Probabilities ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Data Set ◽  
Local Cluster ◽  
Public Data ◽  
Sleep Stage Classification ◽  
Stage Transition

：Sleep stage on the whole night is not steady. Sleepers generally pass through three to five cycles. In each cycle, there are occur four typical sleep stages, such as wake stage (WS), light stage (LS), deep sleep (DS), rapid eye movement sleep stage (REM). According to the natural routine, in this paper, we investigate the stage transition and analyze the feature of stage transition using the local cluster Algorithm (LCA). Two-cycle sleep model (TCSM) is proposed to automatically classify sleep stages using over-night continuous heart rate variability (HRV) data. The generated model is based on the characteristics of the nested cycle's sleep stage distribution and the transition probabilities of sleep stages. Experiments were conducted using a public data set including 400 healthy subjects (female 239, male 161) and the model&rsquo;s classification accuracy was evaluated for four sleep stages: WS, LS, DS, REM. The experimental results showed that based on the TCSM model, the segmentation classification of pure sleep is 5.2% higher than that of the traditional method, and the accuracy of segmentation classification is 11.2% higher than the traditional sleep staging accuracy. The experimental performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art methods. The study contributes to improve the quality of sleep monitoring in daily life using easy-to-wear HRV sensors.

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 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.

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