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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.
Supplementary materials
Figure S1 Schematic of signal processing of respiration signals.
Abbreviations: AXL, accelerometer; BRC, BiostampRC® sensor system; LP, low pass; PCA, principle component analysis; RMSE, root mean square error; RR, respiration rate; x, y, z, axes of the accelerometer; SQI, signal quality index.
Figure S2 BiostampRC® device.
Note: Left, position on the lower leg; Right, close-up view of one device.
Figure S3 The true positive (TP) rate (ie, sensitivity) is shown on the y-axis, and the FP rate (ie, 1-speciificity) is shown on the x-axis
Abbreviations: AUC, area under the curve; EMG, electromyography; PSG, polysomnography; RMS, root mean square; TP, true positive.
Acknowledgments
MC10, Inc. funded this project.
Disclosure
Dr Bianchi has received funding from the Department of Neurology (MGH), Milton Foundation, American Sleep Medicine Foundation, MGHMIT Grand Challenge, and the Center for Integration of Medicine and Innovative Technology; has research contracts with Insomnisolv, Inc.; has consulting agreements with McKesson Health, International Flavors and Fragrances, and Apple Inc.; has received payment for lectures from Oakstone Publishing; has served as a Medical Monitor for Pfizer, Inc; and has provided expert testimony in sleep medicine. These entities had no role in the current study. The authors report no other conflicts of interest in this work.