A Novel Adaptive Wrist Actigraphy Algorithm for Sleep-Wake Assessment in Sleep Apnea Patients

Hedner, Jan; Pillar, Giora; Pittman, Stephen D.; Zou, Ding; Grote, Ludger; White, David P.

doi:10.1093/sleep/27.8.1560

Cited by 189 publications

(150 citation statements)

References 23 publications

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“…5 It has been shown to accurately detect OSA (in laboratory and ambulatory settings), [5][6][7][8] autonomic arousals, 9 and sleep/wake status. 10 The validity of using PAT and actigraphy signals derived from the PAT recorder to detect REM 11 and light/deep 12 sleep has also been demonstrated in two small studies. In the current study, we aimed to validate the sleep staging algorithm of the PAT recorder device in a large population including normal subjects and patients with suspected OSA.…”

Section: S C I E N T I F I C I N V E S T I G a T I O N Smentioning

confidence: 93%

“…Then, the NREM sleep epochs were further categorized as either light (mirror sleep stage 1 and 2) or deep (mirror sleep stage 3 and 4) sleep. The detailed algorithms of sleep/wake detection from actigraphy signal 10 and sleep staging from the PAT signal 11,12 have previously been described.…”

Section: Automatic Zzzpat Algorithmmentioning

confidence: 99%

“…Detailed information on this multi-center study cohort has been previously reported. 10 In brief, the cohort consisted of 17 normal volunteers, 139 patients referred to the sleep laboratory due to suspected OSA, and 71 subjects randomly drawn from a population based cohort of subjects undergoing ambulatory PSG studies to investigate the association between sleep disordered breathing and blood pressure. The original population cohort included normotensive subjects (random population sample) and patients with hypertension.…”

Section: Overview Of Study Designmentioning

confidence: 99%

“…5,10 Briefly, this battery-powered, wrist-mounted device records PAT signal (finger arterial pulse wave volume), pulse rate derived from the PAT signal, oxyhemoglobin saturation, and wrist activity (derived from actigraphy). A continuous synchronization signal generated by the PAT recorder was recorded on both the PAT recorder device and the PSG for epoch-byepoch comparison.…”

Section: Wp100 Recordingmentioning

confidence: 99%

“…While most portable devices do not assess sleep or sleep stages, detect wakefulness, light sleep, deep sleep, and REM sleep. While separate components of the sleep stage algorithm have been assessed previously, [10][11][12] this is the first PSG-based validation study using a complete software package (zzzPAT) for sleep staging in normal subjects and patients with different degree of OSA severity.…”

Section: Light/deep Sleep Detectionmentioning

confidence: 99%

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Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Hedner

White

Malhotra

et al. 2011

Journal of Clinical Sleep Medicine

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123

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Study Objectives: One of the most important caveats of ambulatory devices is the inability to record and stage sleep. We assessed an algorithm determining 4 different stages: wake, light sleep, deep sleep, and REM sleep using signals derived from the portable monitor Watch-PAT100 (PAT recorder). Methods: Participants (38 normal subjects and 189 patients with obstructive sleep apnea [OSA]) underwent simultaneous overnight recordings with polysomnography (PSG) and the PAT recorder in a study originally designed to assess the accuracy of the PAT recorder in diagnosing OSA. Light/deep sleep and REM sleep from the PAT recorder recording were automatically scored based on features extracted from time series of peripheral arterial tone amplitudes and inter pulse periods. The PSG scored sleep stages 1 and 2 were classified as light sleep for epoch-by-epoch comparisons. Results:The overall agreement in detecting light/deep and REM sleep were 88.6% ± 5.9% and 88.7% ± 5.5%, respectively. There was a good agreement between PSG and the PAT recorder in quantifying sleep efficiency (78.4% ± 9.9% vs. 78.8% ± 13.4%), REM latency (237 ± 148 vs. 225 ± 159 epochs), and REM percentage (14.4% ± 6.5% vs. 19.3% ± 8.7%). OSA severity did not affect the sensitivity and specificity of the algorithm. The Cohen κ coefficient for detecting all sleep stages: sleep from wake, REM from NREM sleep, and deep from light sleep were 0.48, 0.55, 0.59, and 0.46, respectively. Conclusions: Analysis of autonomic signals from the PAT recorder can detect sleep stages with moderate agreement to more standard techniques in normal subjects and OSA patients. This novel algorithm may provide insights on sleep and sleep architecture when applying the PAT recorder for OSA diagnosis.

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Section: S C I E N T I F I C I N V E S T I G a T I O N Smentioning

confidence: 93%

Section: Automatic Zzzpat Algorithmmentioning

confidence: 99%

Section: Overview Of Study Designmentioning

confidence: 99%

Section: Wp100 Recordingmentioning

confidence: 99%

Section: Light/deep Sleep Detectionmentioning

confidence: 99%

See 3 more Smart Citations

Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Hedner

White

Malhotra

et al. 2011

Journal of Clinical Sleep Medicine

Self Cite

123

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Short Sleep Duration and Incident Coronary Artery Calcification

King

Knutson

Rathouz

et al. 2008

JAMA

317

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Context Coronary artery calcification is a subclinical predictor of coronary heart disease. Recent studies have found that sleep duration is correlated with established risk factors for calcification including glucose regulation, blood pressure, sex, age, education, and body mass index. Objective To determine whether objective and subjective measures of sleep duration and quality are associated with incidence of calcification over five years and whether calcification risk factors mediate the association. Design Observational cohort 2000–2006. Potential confounders (age, sex, race, education, apnea risk, smoking status) and mediators (lipids, blood pressure, body mass index, diabetes, inflammatory markers, alcohol, depression, hostility, self reported medical conditions) were measured at both baseline and follow-up. We examined sleep metrics (wrist actigraphy measured duration and fragmentation, daytime sleepiness, overall quality, self-reported duration) for association with incident calcification. Setting Home monitoring of healthy middle-aged population. Participants 494 participants from the CARDIA cohort Chicago site (African American and white men and women aged 35–47 at baseline) with sleep, demographic and calcification measurements and no detectable baseline calcification. Outcome Coronary artery calcification was measured by CT in 2000–2001 and 2005–2006; incidence of new calcification over that time was the primary outcome. Results Five-year calcification incidence was 12.3% (n=61). Longer measured sleep duration was significantly associated with decreased adjusted odds of calcification (OR = 0.67 per hour, p=.011; 95% CI 0.49–0.91). No potential mediators appreciably altered the magnitude or significance of sleep (adjusted OR estimates ranged from 0.64 to 0.68 per sleep hour, maximum p-value=.020). Alternative sleep metrics were not significantly associated with calcification. Conclusions Longer measured sleep is associated with lower calcification incidence independently of examined potential mediators and confounders.

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Relationship Between Sleep Duration and Incident Coronary Artery Calcification

Vichayavilas

Kelly

2009

JAMA

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A Novel Adaptive Wrist Actigraphy Algorithm for Sleep-Wake Assessment in Sleep Apnea Patients

Cited by 189 publications

References 23 publications

Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Sleep Staging Based on Autonomic Signals: A Multi-Center Validation Study

Short Sleep Duration and Incident Coronary Artery Calcification

Relationship Between Sleep Duration and Incident Coronary Artery Calcification

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