Dataset Information

A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography.

ABSTRACT:

Background

Although in-lab polysomnography (PSG) remains the gold standard for objective sleep monitoring, the use of at-home sensor systems has gained popularity in recent years. Two categories of monitoring, autonomic and limb movement physiology, are increasingly recognized as critical for sleep disorder phenotyping, yet at-home options remain limited outside of research protocols. The purpose of this study was to validate the BiostampRC^® sensor system for respiration, electrocardiography (ECG), and leg electromyography (EMG) against gold standard PSG recordings.

Methods

We report analysis of cardiac and respiratory data from 15 patients and anterior tibialis (AT) data from 19 patients undergoing clinical PSG for any indication who simultaneously wore BiostampRC^® sensors on the chest and the bilateral AT muscles. BiostampRC^® is a flexible, adhesive, wireless sensor capable of capturing accelerometry, ECG, and EMG. We compared BiostampRC^® data and feature extractions with those obtained from PSG.

Results

The heart rate extracted from BiostampRC^® ECG showed strong agreement with the PSG (cohort root mean square error of 5 beats per minute). We found the thoracic BiostampRC^® respiratory waveform, derived from its accelerometer, accurately calculated the respiratory rate (mean average error of 0.26 and root mean square error of 1.84 breaths per minute). The AT EMG signal supported periodic limb movement detection, with area under the curve of the receiver operating characteristic curve equaling 0.88. Upon completion, 88% of subjects indicated willingness to wear BiostampRC^® at home on an exit survey.

Conclusion

The results demonstrate that BiostampRC^® is a tolerable and accurate method for capturing respiration, ECG, and AT EMG time series signals during overnight sleep when compared with simultaneous PSG recordings. The signal quality sufficiently supports analytics of clinical relevance. Larger longitudinal in-home studies are required to support the role of BiostampRC^® in clinical management of sleep disorders involving the autonomic nervous system and limb movements.

SUBMITTER: Jortberg E

PROVIDER: S-EPMC6263294 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography.

Jortberg Elise E Silva Ikaro I Bhatkar Viprali V McGinnis Ryan S RS Sen-Gupta Ellora E Morey Briana B Wright John A JA Pindado Jesus J Bianchi Matt T MT

Nature and science of sleep 20181126

<h4>Background</h4>Although in-lab polysomnography (PSG) remains the gold standard for objective sleep monitoring, the use of at-home sensor systems has gained popularity in recent years. Two categories of monitoring, autonomic and limb movement physiology, are increasingly recognized as critical for sleep disorder phenotyping, yet at-home options remain limited outside of research protocols. The purpose of this study was to validate the BiostampRC<sup>®</sup> sensor system for respiration, elec ...[more]

PMID: 30538592

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Project description:Study objectivesTo compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents.MethodsFifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epoch-by-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG.ResultsFitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ≤ 38 minutes, Fitbit ≤ 47 minutes; WASO: M10 ≤ 38 minutes; Fitbit ≤ 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ≤ 12 minutes; WASO: ≤ 18 minutes). Sensitivity for detecting sleep was ≥ 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (≥ 0.88), followed by the AW2 at M10 (≥ 0.80) and H5 thresholds (≤ 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ≥ 0.68, ≥ 0.50, and ≥ 0.72, respectively.ConclusionsA consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents.Clinical trial registrationRegistry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://clinicaltrials.gov/ct2/show/NCT03333512.CitationLee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.

Dataset Information

A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography.

Background

Methods

Results

Conclusion

Publications

A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography.

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