Abstract
Objective
Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea–Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO2 signals using a large (n = 412) dataset serving as ground truth.
Design
Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%) feature, one allowing a time lag of 30 s between the two signals.
Results
Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively.
Conclusions
A wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.
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Funding
M.B.W. was supported by the Glenn Foundation for Medical Research and American Federation for Aging Research (Breakthroughs in Gerontology Grant); the American Academy of Sleep Medicine (AASM Foundation Strategic Research Award); the Football Players Health Study (FPHS) at Harvard University; the Department of Defense through a subcontract from Moberg ICU Solutions, Inc.; and by the NIH (1R01NS102190, 1R01NS102574, 1R01NS107291, 1RF1AG064312).
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Contributions
WG: developed code and models, performed analysis, and drafted and reviewed the manuscript.
AB: performed analysis, drafted and reviewed the manuscript, and was part of data acquisition.
RAT: was responsible for data management, data acquisition, critical review and revision of the manuscript.
MDSC: was part of the data analysis, critical review and revision of the manuscript.
HS: was part of the data analysis, critical review and revision of the manuscript.
MJL: was part of the data management, critical review and revision of the manuscript.
LP: was part of data acquisition, critical review and revision of the manuscript.
EP: was part of data acquisition, critical review and revision of the manuscript.
EMY: was part of the data management, critical review and revision of the manuscript.
BTT: critical review and revision of the manuscript.
OA: critical review and revision of the manuscript.
DK: inventor of the AirGo device, critical review and revision of the manuscript.
RJT: design of the work, supervision (mentorship), critical review and revision of the manuscript.
MBW: design of the work, supervision (oversight and leadership), critical review and revision of the manuscript.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all individual participants included in the study.
Conflict of interest
Wolfgang Ganglberger declares that he/she has no conflict of interest. Abigail A. Bucklin declares that he/she has no conflict of interest. Ryan A. Tesh declares that he/she has no conflict of interest. Madalena Da Silva Cardoso declares that he/she has no conflict of interest. Haoqi Sun declares that he/she has no conflict of interest. Michael J. Leone declares that he/she has no conflict of interest. Luis Paixao declares that he/she has no conflict of interest. Ezhil Panneerselvam declares that he/she has no conflict of interest. Elissa M. Ye declares that he/she has no conflict of interest. B. Taylor Thompson reports personal fees from Bayer and Thetis, outside the submitted work. Oluwaseun Akeju declares that he/she has no conflict of interest. David Kuller reports non-financial support from Myair Inc., during the conduct of the study; non-financial support from Myair Inc., outside the submitted work; in addition, Dr. Kuller has a Patent US10123724B2 “Breath volume monitoring system and method” issued. Dr. Thomas reports personal fees from GLG Councils, Guidepoint Global, and Jazz Pharmaceutics, outside the submitted work. In addition, Dr. Thomas has a patent ECG-spectrogram with royalties paid by MyCardio, LLC, a patent Auto-CPAP with royalties paid by DeVilbiss-Drive, and an unlicensed patent CO2 device for central/complex sleep apnea issued. Dr. Westover reports grants from NIH, during the conduct of the study.
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Ganglberger, W., Bucklin, A.A., Tesh, R.A. et al. Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation. Sleep Breath 26, 1033–1044 (2022). https://doi.org/10.1007/s11325-021-02465-2
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DOI: https://doi.org/10.1007/s11325-021-02465-2