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A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263294/ https://www.ncbi.nlm.nih.gov/pubmed/30538592 http://dx.doi.org/10.2147/NSS.S179588 |
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author | Jortberg, Elise Silva, Ikaro Bhatkar, Viprali McGinnis, Ryan S Sen-Gupta, Ellora Morey, Briana Wright, John A Pindado, Jesus Bianchi, Matt T |
author_facet | Jortberg, Elise Silva, Ikaro Bhatkar, Viprali McGinnis, Ryan S Sen-Gupta, Ellora Morey, Briana Wright, John A Pindado, Jesus Bianchi, Matt T |
author_sort | Jortberg, Elise |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6263294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62632942018-12-11 A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography Jortberg, Elise Silva, Ikaro Bhatkar, Viprali McGinnis, Ryan S Sen-Gupta, Ellora Morey, Briana Wright, John A Pindado, Jesus Bianchi, Matt T Nat Sci Sleep Original Research 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. Dove Medical Press 2018-11-26 /pmc/articles/PMC6263294/ /pubmed/30538592 http://dx.doi.org/10.2147/NSS.S179588 Text en © 2018 Jortberg et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. |
spellingShingle | Original Research Jortberg, Elise Silva, Ikaro Bhatkar, Viprali McGinnis, Ryan S Sen-Gupta, Ellora Morey, Briana Wright, John A Pindado, Jesus Bianchi, Matt T A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title | A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title_full | A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title_fullStr | A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title_full_unstemmed | A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title_short | A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
title_sort | novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263294/ https://www.ncbi.nlm.nih.gov/pubmed/30538592 http://dx.doi.org/10.2147/NSS.S179588 |
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