{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T18:53:56Z","timestamp":1781636036504,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"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":["Sleep Breath"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), is prevalent in hospitalized patients, yet remains underdiagnosed due to limited screening tools. This pilot study aimed to develop and validate a machine-learning classifier using electrocardiogram (ECG) and pulse oximetry (SpO\u2082) waveforms to detect SDB, leveraging routinely collected physiological data to enable non-invasive, scalable screening in hospitalized patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We utilized data from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep cohort, which included full overnight polysomnography (PSG). A total of 122 participants were randomly selected, enriched for severe OSA cases. Spectral analysis of R-R intervals and SpO\u2082 variability was performed, and a support vector machine (SVM) classifier was trained using a subset of subjects (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200930). Performance was evaluated in a validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200992) using standard classification metrics with 95% CIs calculated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The first-stage classifier (SVM-1) demonstrated high sensitivity (98.7%) and specificity (99.0%) in identifying respiratory events at the window level. The second-stage classifier (SVM-2) correctly classified OSA presence with 100% accuracy in the training set. When applied to the validation set, the combined model achieved a sensitivity of 72.3%, specificity of 73.3%, and F1-score of 73.1%. Performance was impacted by arrhythmias, and lack of direct respiratory effort measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A machine-learning model using routinely collected ECG and SpO\u2082 data shows promise for SDB detection but may require additional cardiopulmonary parameters for clinical decision-making in the ICU. Further validation with data collected in real-world ICU settings is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11325-026-03602-5","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:33:13Z","timestamp":1770834793000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectral analysis of ECG and SpO\u2082 for machine learning classification of Sleep-Disordered breathing"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8178-3240","authenticated-orcid":false,"given":"Zachary B.","family":"Strumpf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhad","family":"Kaffashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susheel P.","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kingman P.","family":"Strohl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth A.","family":"Loparo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank J.","family":"Jacono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"issue":"8","key":"3602_CR1","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","volume":"7","author":"AV Benjafield","year":"2019","unstructured":"Benjafield AV, Ayas NT, Eastwood PR et al (2019) Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. 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All participants in the MESA study provided written informed consent for future research use of their data as approved by the original investigators.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"41"}}