{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T07:14:25Z","timestamp":1777792465549,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of <jats:italic>n<\/jats:italic>\u2009=\u20093806 individual polysomnography recordings totaling 26,686\u2009h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\overline R ^2 = 0.82$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mrow>\n                        <mml:mover>\n                          <mml:mrow>\n                            <mml:mi>R<\/mml:mi>\n                          <\/mml:mrow>\n                          <mml:mo>\u00af<\/mml:mo>\n                        <\/mml:mover>\n                      <\/mml:mrow>\n                      <mml:mrow>\n                        <mml:mn>2<\/mml:mn>\n                      <\/mml:mrow>\n                    <\/mml:msup>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mn>0.82<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. The resulting python OBM toolbox, denoted \u201cpobm\u201d, was contributed to the open software PhysioZoo (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/physiozoo.org\">physiozoo.org<\/jats:ext-link>). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.<\/jats:p>","DOI":"10.1038\/s41746-020-00373-5","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T11:02:41Z","timestamp":1609758161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":108,"title":["Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7498-6413","authenticated-orcid":false,"given":"Jeremy","family":"Levy","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1027-2395","authenticated-orcid":false,"given":"Daniel","family":"\u00c1lvarez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3755-6534","authenticated-orcid":false,"given":"Aviv A.","family":"Rosenberg","sequence":"additional","affiliation":[]},{"given":"Alexandra","family":"Alexandrovich","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4554-2167","authenticated-orcid":false,"given":"F\u00e9lix","family":"del Campo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5956-7034","authenticated-orcid":false,"given":"Joachim A.","family":"Behar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"373_CR1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1053\/smrv.2002.0261","volume":"7","author":"L Lavie","year":"2003","unstructured":"Lavie, L. Obstructive sleep apnoea syndrome - an oxidative stress disorder. Sleep Med. Rev. 7, 35\u201351 (2003).","journal-title":"Sleep Med. Rev."},{"key":"373_CR2","doi-asserted-by":"publisher","first-page":"259","DOI":"10.5664\/jcsm.4540","volume":"11","author":"R Budhiraja","year":"2015","unstructured":"Budhiraja, R., Siddiqi, T. A. & Quan, S. F. Sleep disorders in chronic obstructive pulmonary disease: etiology, impact, and management. J. Clin. Sleep Med. 11, 259\u2013270 (2015).","journal-title":"J. Clin. Sleep Med."},{"key":"373_CR3","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","volume":"17","author":"M Malik","year":"1996","unstructured":"Malik, M. et al. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur. Hear. J. 17, 354\u2013381 (1996).","journal-title":"Eur. Hear. J."},{"key":"373_CR4","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.cmpb.2013.07.024","volume":"113","author":"MP Tarvainen","year":"2014","unstructured":"Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O. & Karjalainen, P. A. Kubios HRV - heart rate variability analysis software. Comput. Methods Prog. Biol. 113, 210\u201320 (2014).","journal-title":"Comput. Methods Prog. Biol."},{"key":"373_CR5","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.jelectrocard.2017.08.006","volume":"50","author":"AN Vest","year":"2017","unstructured":"Vest, A. N. et al. Benchmarking heart rate variability toolboxes. J. Electrocardiol. 50, 744\u2013747 (2017).","journal-title":"J. Electrocardiol."},{"key":"373_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2018.01390","volume":"9","author":"JA Behar","year":"2018","unstructured":"Behar, J. A. et al. PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data. Front. Physiol. 9, 1\u201314 (2018).","journal-title":"Front. Physiol."},{"key":"373_CR7","doi-asserted-by":"publisher","first-page":"2018","DOI":"10.1088\/1361-6579\/aaafb8","volume":"39","author":"MB Uddin","year":"2018","unstructured":"Uddin, M. B., Chow, C. M. & Su, S. W. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review. Physiol. Meas. 39, 2018 (2018).","journal-title":"Physiol. Meas."},{"key":"373_CR8","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1080\/17476348.2018.1495563","volume":"12","author":"F del Campo","year":"2018","unstructured":"del Campo, F. et al. Oximetry use in obstructive sleep apnea. Expert Rev. Resp. Med. 12, 665\u2013681 (2018).","journal-title":"Expert Rev. Resp. Med."},{"key":"373_CR9","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1109\/JBHI.2018.2823265","volume":"23","author":"F Mendon\u00e7a","year":"2019","unstructured":"Mendon\u00e7a, F., Mostafa, S. S., Ravelo-Garc\u00eda, A. G., Morgado-Dias, F. & Penzel, T. A review of obstructive sleep apnea detection approaches. IEEE J. Biomed. Health Inform. 23, 825\u2013837 (2019).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"373_CR10","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1111\/resp.13635","volume":"25","author":"PI Terrill","year":"2020","unstructured":"Terrill, P. I. A review of approaches for analysing obstructive sleep apnoea-related patterns in pulse oximetry data. Respirology 25, 475\u2013485 (2020).","journal-title":"Respirology"},{"key":"373_CR11","doi-asserted-by":"publisher","unstructured":"Rashid, N. H. et al. The value of oxygen desaturation index for diagnosing obstructive sleep apnea: a systematic review. Laryngoscope. https:\/\/doi.org\/10.1002\/lary.28663 (2020).","DOI":"10.1002\/lary.28663"},{"key":"373_CR12","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1093\/eurheartj\/ehy624","volume":"40","author":"A Azarbarzin","year":"2019","unstructured":"Azarbarzin, A. et al. The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the osteoporotic fractures in Men Study and the Sleep Heart Health Study. Eur. Heart J. 40, 1149\u20131157 (2019).","journal-title":"Eur. Heart J."},{"key":"373_CR13","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215\u2013E220 (2000).","journal-title":"Circulation"},{"key":"373_CR14","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/s11325-017-1616-0","volume":"22","author":"D Linz","year":"2018","unstructured":"Linz, D. et al. Nocturnal hypoxemic burden is associated with epicardial fat volume in patients with acute myocardial infarction. Sleep Breath. 22, 703\u2013711 (2018).","journal-title":"Sleep Breath."},{"key":"373_CR15","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1378\/chest.14-0500","volume":"147","author":"NA Dewan","year":"2015","unstructured":"Dewan, N. A., Nieto, F. J. & Somers, V. K. Intermittent hypoxemia and OSA: implications for comorbidities. Chest 147, 266\u2013274 (2015).","journal-title":"Chest"},{"key":"373_CR16","first-page":"6939","volume":"24","author":"G Orr\u00f9","year":"2020","unstructured":"Orr\u00f9, G. et al. Obstructive Sleep Apnea, oxidative stress, inflammation and endothelial dysfunction-an overview of predictive laboratory biomarkers. Eur. Rev. Med. Pharmacol. Sci. 24, 6939\u20136948 (2020).","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"373_CR17","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1016\/j.chest.2018.09.030","volume":"155","author":"C Suen","year":"2019","unstructured":"Suen, C. et al. Sleep study and oximetry parameters for predicting postoperative complications in patients with OSA. Chest 155, 855\u2013867 (2019).","journal-title":"Chest"},{"key":"373_CR18","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s11517-013-1039-4","volume":"51","author":"A Kulkas","year":"2013","unstructured":"Kulkas, A., Tiihonen, P., Julkunen, P., Mervaala, E. & T\u00f6yr\u00e4s, J. Novel parameters indicate significant differences in severity of obstructive sleep apnea with patients having similar apnea-hypopnea index. Med. Biol. Eng. Comput. 51, 697\u2013708 (2013).","journal-title":"Med. Biol. Eng. Comput."},{"key":"373_CR19","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1378\/chest.92.4.604","volume":"92","author":"EC Fletcher","year":"1987","unstructured":"Fletcher, E. C., Miller, J., Divine, G. W., Fletcher, J. G. & Miller, T. Nocturnal oxyhemoglobin desaturation in COPD patients with arterial oxygen tensions above 60 mm Hg. Chest 92, 604\u2013608 (1987).","journal-title":"Chest"},{"key":"373_CR20","unstructured":"Resta, O. et al. Sleep related O2 desaturation in COPD patients with normoxaemia and mild hypoxyaemia. Boll. Soc. Ital. Biol. Sper. 74, 91\u201398 (1998)."},{"key":"373_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-020-03185-x","volume":"24","author":"V Quaresima","year":"2020","unstructured":"Quaresima, V. & Ferrari, M. COVID-19: efficacy of prehospital pulse oximetry for early detection of silent hypoxemia. Crit. Care 24, 501 (2020).","journal-title":"Crit. Care"},{"key":"373_CR22","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/JBHI.2018.2817368","volume":"23","author":"M Deviaene","year":"2019","unstructured":"Deviaene, M. et al. Automatic screening of sleep apnea patients based on the SpO2 signal. IEEE J. Biomed. Health Inform. 23, 607\u2013617 (2019).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"373_CR23","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1093\/sleep\/20.11.991","volume":"20","author":"BH Taha","year":"1997","unstructured":"Taha, B. H. et al. Automated detection and classification of sleep-disordered breathing from conventional polysomnography data. Sleep 20, 991\u20131001 (1997).","journal-title":"Sleep"},{"key":"373_CR24","first-page":"1","volume":"21","author":"J Buekers","year":"2019","unstructured":"Buekers, J. et al. Wearable finger pulse oximetry for continuous oxygen saturation measurements during daily home routines of patients with chronic obstructive pulmonary disease (COPD) over one week: observational study. J. Med. Internet Res. 21, 1\u201314 (2019).","journal-title":"J. Med. Internet Res."},{"key":"373_CR25","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TITB.2012.2188299","volume":"16","author":"B Xie","year":"2012","unstructured":"Xie, B. & Minn, H. Real-time sleep apnea detection by classifier combination. IEEE Trans. Inf. Technol. Biomed. 16, 469\u2013477 (2012).","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"373_CR26","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1378\/chest.99.5.1151","volume":"99","author":"JL Pepin","year":"1991","unstructured":"Pepin, J. L., Levy, P., Lepaulle, B., Brambilla, C. & Guilleminault, C. Does oximetry contribute to the detection of apneic events? Mathematical processing of the SaO2 signal. Chest 99, 1151\u20131157 (1991).","journal-title":"Chest"},{"key":"373_CR27","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1378\/chest.124.5.1694","volume":"124","author":"UJ Magalang","year":"2003","unstructured":"Magalang, U. J. et al. Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest 124, 1694\u20131701 (2003).","journal-title":"Chest"},{"key":"373_CR28","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","volume":"88","author":"SM Pincus","year":"1991","unstructured":"Pincus, S. M. Approximate entropy as a measure of system complexity. Proc. Natl Acad. Sci. USA 88, 2297\u20132301 (1991).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"373_CR29","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman, J. S. & Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. 278, H2039\u2013H2049 (2000).","journal-title":"Am. J. Physiol. Heart Circ."},{"key":"373_CR30","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/S0197-4580(01)00266-4","volume":"23","author":"AL Goldberger","year":"2002","unstructured":"Goldberger, A. L., Peng, C. K. & Lipsitz, L. A. What is physiologic complexity and how does it change with aging and disease? Neurobiol. Aging 23, 23\u201326 (2002).","journal-title":"Neurobiol. Aging"},{"key":"373_CR31","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/TIT.1976.1055501","volume":"22","author":"A Lempel","year":"1976","unstructured":"Lempel, A. & Ziv, J. On the complexity of finite sequences. IEEE Trans. Inf. Theory 22, 75\u201381 (1976).","journal-title":"IEEE Trans. Inf. Theory"},{"key":"373_CR32","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/scientificamerican0290-42","volume":"262","author":"L Ary","year":"1990","unstructured":"Ary, L., Goldberger, D. R. R. & B., J. Chaos and fractals in human physiology. Sci. Am. 262, 42\u201349 (1990).","journal-title":"Sci. Am."},{"key":"373_CR33","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TBME.2006.883821","volume":"54","author":"R Hornero","year":"2007","unstructured":"Hornero, R., \u00c1lvarez, D., Ab\u00e1solo, D., Del Campo, F. & Zamarr\u00f3n, C. Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome. IEEE Trans. Biomed. Eng. 54, 107\u2013113 (2007).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"373_CR34","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1162\/089976604322860677","volume":"16","author":"JM Amig\u00f3","year":"2004","unstructured":"Amig\u00f3, J. M., Szczepa\u0144ski, J., Wajnryb, E. & Sanchez-Vives, M. V. Estimating the entropy rate of spike trains via Lempel-Ziv complexity. Neural Comput. 16, 717\u2013736 (2004).","journal-title":"Neural Comput."},{"key":"373_CR35","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1088\/0967-3334\/27\/4\/006","volume":"27","author":"D \u00c1lvarez","year":"2006","unstructured":"\u00c1lvarez, D., Hornero, R., Ab\u00e1solo, D., Del Campo, F. & Zamarr\u00f3n, C. Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection. Physiol. Meas. 27, 399\u2013412 (2006).","journal-title":"Physiol. Meas."},{"key":"373_CR36","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1063\/1.166141","volume":"5","author":"CK Peng","year":"1995","unstructured":"Peng, C. K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82\u201387 (1995).","journal-title":"Chaos"},{"key":"373_CR37","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/51.537065","volume":"15","author":"ME Cohen","year":"1996","unstructured":"Cohen, M. E., Hudson, D. L. & Deedwania, P. C. Applying continuous chaotic modeling to cardiac signal analysis. IEEE Eng. Med. Biol. Mag. 15, 97\u2013102 (1996).","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"373_CR38","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.physa.2005.08.080","volume":"364","author":"A Bauer","year":"2006","unstructured":"Bauer, A. et al. Phase-rectified signal averaging detects quasi-periodicities in non-stationary data. Phys. A Stat. Mech. Appl. 364, 423\u2013434 (2006).","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"373_CR39","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1093\/jamia\/ocy064","volume":"25","author":"GQ Zhang","year":"2018","unstructured":"Zhang, G. Q. et al. The National Sleep Research Resource: towards a sleep data commons. J. Am. Med. Inform. Assoc. 25, 1351\u20131358 (2018).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"373_CR40","first-page":"1077","volume":"20","author":"S Quan","year":"1997","unstructured":"Quan, S. et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep 20, 1077\u20131085 (1997).","journal-title":"Sleep"},{"key":"373_CR41","first-page":"759","volume":"21","author":"S Redline","year":"1998","unstructured":"Redline, S. et al. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Res. Group Sleep 21, 759\u2013767 (1998).","journal-title":"Sleep Heart Health Res. Group Sleep"},{"key":"373_CR42","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.5665\/sleep.5774","volume":"39","author":"DA Dean","year":"2016","unstructured":"Dean, D. A. et al. Scaling up scientific discovery in sleep medicine: The National Sleep Research Resource. Sleep 39, 1151\u20131164 (2016).","journal-title":"Sleep"},{"key":"373_CR43","first-page":"255","volume":"27","author":"T Penzel","year":"2000","unstructured":"Penzel, T., Rg, G. B. M., Goldberges, M. A. L. & Peter, H. The Apnea-ECG Database. Comput. Cardiol. 27, 255\u2013258 (2000).","journal-title":"Comput. Cardiol."},{"key":"373_CR44","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1378\/chest.123.5.1567","volume":"123","author":"C Zamarr\u00f3n","year":"2003","unstructured":"Zamarr\u00f3n, C., Gude, F., Barcala, J., Rodriguez, J. R. & Romero, P. V. Utility of oxygen saturation and heart rate spectral analysis obtained from pulse oximetric recordings in the diagnosis of sleep apnea syndrome. Chest 123, 1567\u20131576 (2003).","journal-title":"Chest"},{"key":"373_CR45","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1042\/CS19980367","volume":"97","author":"C Zamarr\u00f3n Sanz","year":"1999","unstructured":"Zamarr\u00f3n Sanz, C., Romero, P. V., Rodriguez, J. R. & Gude, F. Oximetry spectral analysis in the diagnosis of obstructive sleep apnoea. Clin. Sci. 97, 467\u2013473 (1999).","journal-title":"Clin. Sci."},{"key":"373_CR46","first-page":"2077","volume":"9","author":"PD Welch","year":"1967","unstructured":"Welch, P. D. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 9, 2077 (1967).","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"373_CR47","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1088\/1361-6579\/aa7b6f","volume":"38","author":"A Kulkas","year":"2017","unstructured":"Kulkas, A., Duce, B., Lepp\u00e4nen, T., Hukins, C. & T\u00f6yr\u00e4s, J. Gender differences in severity of desaturation events following hypopnea and obstructive apnea events in adults during sleep. Physiol. Meas. 38, 1490\u20131502 (2017).","journal-title":"Physiol. Meas."},{"key":"373_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/sleep\/zsz279","volume":"43","author":"JA Behar","year":"2020","unstructured":"Behar, J. A. From sleep medicine to medicine during sleep: a new paradigm. Sleep 43, 1\u20133 (2020).","journal-title":"Sleep"},{"key":"373_CR49","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.eclinm.2019.05.015","volume":"11","author":"JA Behar","year":"2019","unstructured":"Behar, J. A. et al. Feasibility of single channel oximetry for mass screening of obstructive sleep apnea. EClinicalMedicine 11, 81\u201388 (2019).","journal-title":"EClinicalMedicine"},{"key":"373_CR50","first-page":"4","volume":"41","author":"JA Behar","year":"2020","unstructured":"Behar, J. A., Palmius, N., Penzel, T., Bittencourt, L. & Tufik, S. Single-channel oximetry monitor versus in-lab polysomnography oximetry analysis: does it make a difference? Accept. Physiol. Meas. 41, 4 (2020).","journal-title":"Accept. Physiol. Meas."},{"key":"373_CR51","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/TBME.2017.2715405","volume":"65","author":"DW Jung","year":"2018","unstructured":"Jung, D. W. et al. Real-time automatic apneic event detection using nocturnal pulse oximetry. IEEE Trans. Biomed. Eng. 65, 706\u2013712 (2018).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"373_CR52","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1378\/chest.87.4.432","volume":"87","author":"LJ Findley","year":"1985","unstructured":"Findley, L. J., Wilhoit, S. C. & Suratt, R. M. Apnea duration and hypoxemia during REM sleep in patients with obstructive sleep apnea. Chest 87, 432\u2013436 (1985).","journal-title":"Chest"},{"key":"373_CR53","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1002\/ppul.23987","volume":"53","author":"PI Terrill","year":"2018","unstructured":"Terrill, P. I., Dakin, C., Edwards, B. A., Wilson, S. J. & MacLean, J. E. A graphical method for comparing nocturnal oxygen saturation profiles in individuals and populations: application to healthy infants and preterm neonates. Pediatr. Pulmonol. 53, 645\u2013655 (2018).","journal-title":"Pediatr. Pulmonol."},{"key":"373_CR54","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.cardfail.2016.09.004","volume":"23","author":"E Watanabe","year":"2017","unstructured":"Watanabe, E. et al. Prognostic importance of novel oxygen desaturation metrics in patients with heart failure and central sleep apnea. J. Card. Fail. 23, 131\u2013137 (2017).","journal-title":"J. Card. Fail."},{"key":"373_CR55","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1046\/j.1365-2869.1999.00134.x","volume":"8","author":"LG Olson","year":"1999","unstructured":"Olson, L. G., Ambrogetti, A. & Gyulay, S. G. Prediction of sleep-disordered breathing by unattended overnight oximetry. J. Sleep Res. 8, 51\u201355 (1999).","journal-title":"J. Sleep Res."},{"key":"373_CR56","doi-asserted-by":"publisher","first-page":"597","DOI":"10.5664\/jcsm.2172","volume":"8","author":"RB Berry","year":"2012","unstructured":"Berry, R. B. et al. Rules for scoring respiratory events in sleep: Update of the 2007 AASM manual for the scoring of sleep and associated events. J. Clin. Sleep Med. 8, 597\u2013619 (2012).","journal-title":"J. Clin. Sleep Med."},{"key":"373_CR57","doi-asserted-by":"crossref","unstructured":"Deviaene, M. et al. Pulse oximetry markers for cardiovascular disease in sleep apnea. In 2019 Computing in Cardiology (CinC) 3\u20136 (IEEE, 2019).","DOI":"10.22489\/CinC.2019.205"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00373-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00373-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00373-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T06:07:30Z","timestamp":1674799650000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00373-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["373"],"URL":"https:\/\/doi.org\/10.1038\/s41746-020-00373-5","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"13 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"J.A.B. holds shares in SmartCare Analytics Ltd. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}