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

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

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

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 0447 ResTNet: A Robust End-to-End Deep Learning Approach to Sleep Staging of Self Applied Somnography Studies

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A171-A171
Author(s):  
S Æ Jónsson ◽  
E Gunnlaugsson ◽  
E Finssonn ◽  
D L Loftsdóttir ◽  
G H Ólafsdóttir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Quality Data ◽  
Sleep Stages ◽  
Learning Approach ◽  
Sleep Staging ◽  
Self Administration ◽  
Sleep Study ◽  
Sleep Studies ◽  
End To End

Abstract Introduction Sleep stage classifications are of central importance when diagnosing various sleep-related diseases. Performing a full PSG recording can be time-consuming and expensive, and often requires an overnight stay at a sleep clinic. Furthermore, the manual sleep staging process is tedious and subject to scorer variability. Here we present an end-to-end deep learning approach to robustly classify sleep stages from Self Applied Somnography (SAS) studies with frontal EEG and EOG signals. This setup allows patients to self-administer EEG and EOG leads in a home sleep study, which reduces cost and is more convenient for the patients. However, self-administration of the leads increases the risk of loose electrodes, which the algorithm must be robust to. The model structure was inspired by ResNet (He, Zhang, Ren, Sun, 2015), which has been highly successful in image recognition tasks. The ResTNet is comprised of the characteristic Residual blocks with an added Temporal component. Methods The ResTNet classifies sleep stages from the raw signals using convolutional neural network (CNN) layers, which avoids manual feature extraction, residual blocks, and a gated recurrent unit (GRU). This significantly reduces sleep stage prediction time and allows the model to learn more complex relations as the size of the training data increases. The model was developed and validated on over 400 manually scored sleep studies using the novel SAS setup. In developing the model, we used data augmentation techniques to simulate loose electrodes and distorted signals to increase model robustness with regards to missing signals and low quality data. Results The study shows that applying the robust ResTNet model to SAS studies gives accuracy > 0.80 and F1-score > 0.80. It outperforms our previous model which used hand-crafted features and achieves similar performance to a human scorer. Conclusion The ResTNet is fast, gives accurate predictions, and is robust to loose electrodes. The end-to-end model furthermore promises better performance with more data. Combined with the simplicity of the SAS setup, it is an attractive option for large-scale sleep studies. Support This work was supported by the Icelandic Centre for Research RANNÍS (175256-0611).

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

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

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

Mindfulness and Behaviour Therapy for Insomnia: An Assessment of Treatment Effect in a Sleep Disorders Clinic Population with Insomnia

2020 ◽  
pp. 1-15
Author(s):  
Allie Peters ◽  
John Reece ◽  
Hailey Meaklim ◽  
Moira Junge ◽  
David Cunnington ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Treatment Effect ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Health Concern ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Subjective Sleep ◽  
Sleep Parameters

Abstract Insomnia is a common major health concern, which causes significant distress and disruption in a person's life. The objective of this paper was to evaluate a 6-week version of Mindfulness-Based Therapy for Insomnia (MBTI) in a sample of people attending a sleep disorders clinic with insomnia, including those with comorbidities. Thirty participants who met the DSM-IV-TR diagnosis of insomnia participated in a 6-week group intervention. Outcome measures were a daily sleep diary and actigraphy during pre-treatment and follow-up, along with subjective sleep outcomes collected at baseline, end-of-treatment, and 3-month follow-up. Trend analyses showed that MBTI was associated with a large decrease in insomnia severity (p < .001), with indications of maintenance of treatment effect. There were significant improvements in objective sleep parameters, including sleep onset latency (p = .005), sleep efficiency (p = .033), and wake after sleep onset (p = .018). Significant improvements in subjective sleep parameters were also observed for sleep efficiency (p = .005) and wake after sleep onset (p < .001). Overall, this study indicated that MBTI can be successfully delivered in a sleep disorders clinic environment, with evidence of treatment effect for both objective and subjective measures of sleep.

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

A Sleep Laboratory Evaluation of the Long-Term Efficacy of Zopiclone

1988 ◽  
Vol 33 (2) ◽  
pp. 103-107 ◽  
Author(s):  
Jonathan A.E. Fleming ◽  
Jean Bourgouin ◽  
Peter Hamilton
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Efficiency ◽  
Laboratory Evaluation ◽  
Sleep Study ◽  
Sleep Studies ◽  
Metallic Taste ◽  
Hypnotic Medication ◽  
Drug Free

Six patients between the ages of 25 and 59, with chronic, primary insomnia received the new, non-benzodiazepine, hypnotic zopiclone continuously for 17 weeks after a drug free interval of 12 nights. To qualify for the study, sleep efficiency, determined by a sleep study on two, consecutive, placebo-controlled nights, had to be less than 75%. Patients evaluated their sleep by questionnaire and had sleep studies completed throughout active treatment. Zopiclone (7.5 mg) increased sleep efficiency by decreasing sleep latency, wakefulness after sleep onset and increasing total sleep time. Sleep architecture was minimally affected by zopiclone treatment; no significant changes in delta or REM sleep were observed. The commonest side effect was a bitter or metallic taste. No significant changes in biological functioning were noted throughout the study period. These findings indicate that zopiclone is a safe and effective hypnotic medication which maintains its effectiveness with protracted use.

The sleep macroarchitecture of children at risk for depression recruited in sleep centers

2012 ◽  
Vol 28 (3) ◽  
pp. 168-173 ◽  
Author(s):  
F. Bat-Pitault ◽  
D. Da Fonseca ◽  
S. Cortese ◽  
Y. Le Strat ◽  
L. Kocher ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
High Risk ◽  
Children And Adolescents ◽  
Depressive Episode ◽  
Risk Group ◽  
Sleep Stage ◽  
Sleep Onset ◽  
High Risk Group ◽  
Sleep Efficiency ◽  
History Of

AbstractObjectiveThe primary aim of this study was to compare the sleep macroarchitecture of children and adolescents whose mothers have a history of depression with children and adolescents whose mothers do not.MethodPolysomnography (PSG) and Holter electroencephalogram (EEG) were used to compare the sleep architecture of 35 children whose mothers had at least one previous depressive episode (19 boys, aged 4–18 years, “high-risk” group) and 25 controls (13 males, aged 4–18 years, “low-risk” group) whose mothers had never had a depressive episode. The total sleep time, wakefulness after sleep onset (WASO), sleep latency, sleep efficiency, number of awakenings per hour of sleep, percentages of time spent in each sleep stage, rapid eye movement (REM) latency and the depressive symptoms of participants were measured.ResultsIn children (4–12 years old), the high-risk group exhibited significantly more depressive symptoms than controls (P = 0.02). However, PSG parameters were not significantly different between high-risk children and controls. In adolescents (13–18 years old), the high-risk subjects presented with significantly more depressive symptoms (P = 0.003), a significant increase in WASO (P = 0.019) and a significant decrease in sleep efficiency compared to controls (P = 0.009).ConclusionThis study shows that children and adolescents born from mothers with a history of at least one depressive episode had significantly more depressive symptoms than controls. However, only high-risk adolescents presented with concurrent alterations of sleep macroarchitecture.

scholarly journals Excessive Fragmentary Myoclonus: Time of Night and Sleep Stage Distributions

1993 ◽  
Vol 20 (2) ◽  
pp. 142-146 ◽  
Author(s):  
Otavio Lins ◽  
Michelle Castonguay ◽  
Wayne Dunham ◽  
Sonya Nevsimalova ◽  
Roger Broughton
Keyword(s):  
Sleep Disorders ◽  
Patient Group ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Fragmentation ◽  
New Technique ◽  
A New Technique

ABSTRACT:Excessive fragmentary myoclonus during sleep consists of high amounts of brief twitch-like movements occurring asynchronously and asymmetrically in different body areas and has been reported to occur in association with a number of sleep disorders. It was analyzed using a new technique of quantification, the fragmentary myoclonus index (FMI). The FMI exhibited high rates in all stages of sleep but with a somewhat lower frequency in slow wave sleep explaining, as well, a significantly lower rate in the first hour after sleep onset compared to later hours. There was no evidence for greater sleep fragmentation or lighter sleep compared to a matched patient group in whom it had not been noted.

scholarly journals Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Shahab Haghayegh ◽  
Sepideh Khoshnevis ◽  
Michael H Smolensky ◽  
Kenneth R Diller ◽  
Richard J Castriotta
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Meta Analysis ◽  
Body Movement ◽  
Sleep Onset ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Exclusion Criteria ◽  
Significant Difference ◽  
Sleep Parameters

BACKGROUND Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. OBJECTIVE We conducted a systematic review of publications reporting on the performance of wristband <italic>Fitbit</italic> models in assessing sleep parameters and stages. METHODS In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword <italic>Fitbit</italic> to identify relevant publications meeting predefined inclusion and exclusion criteria. RESULTS The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging <italic>Fitbit</italic> models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=8.8%, <italic>P</italic>=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=24.0%, <italic>P</italic>=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, <italic>P</italic>&lt;.001; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92) and there was no significant difference in sleep onset latency (SOL; <italic>P</italic>=.37; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92). In reference to PSG, nonsleep-staging <italic>Fitbit</italic> models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation <italic>Fitbit</italic> models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging <italic>Fitbit</italic> models, in comparison to PSG, showed no significant difference in measured values of WASO (<italic>P</italic>=.25; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.92), TST (<italic>P</italic>=.29; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.98), and SE (<italic>P</italic>=.19) but they underestimated SOL (<italic>P</italic>=.03; heterogenicity: I<sup>2</sup>=0%, <italic>P</italic>=.66). Sleep-staging <italic>Fitbit</italic> models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. CONCLUSIONS Sleep-staging <italic>Fitbit</italic> models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram

2010 ◽  
Vol 49 (05) ◽  
pp. 467-472 ◽  
Author(s):  
V. C. Helland ◽  
A. Gapelyuk ◽  
A. Suhrbier ◽  
M. Riedl ◽  
T. Penzel ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Computer Software ◽  
High Rate ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Linear Discriminant ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Computational Procedures ◽  
Visually Based

Summary Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm’s assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.

Sign in / Sign up

Export Citation Format

Share Document