A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography
Received 9 July 2018
Accepted for publication 24 September 2018
Published 26 November 2018 Volume 2018:10 Pages 397—408
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Steven A Shea
Elise Jortberg,1 Ikaro Silva,1 Viprali Bhatkar,1 Ryan S McGinnis,2 Ellora Sen-Gupta,1 Briana Morey,1 John A Wright Jr,1 Jesus Pindado,1 Matt T Bianchi3
1MC10, Inc., Lexington, MA 02421, USA; 2Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA; 3Neurology Department, Massachusetts General Hospital, Boston, MA 02114, USA
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.
Keywords: electrocardiography, electromyography, respiration, wearable
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