{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:31:48Z","timestamp":1778272308477,"version":"3.51.4"},"reference-count":25,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"tdm","delay-in-days":25,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2019,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    ) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    .\n                    <jats:italic>Approach<\/jats:italic>\n                    : One thousand one hundred and nineteen patients from the Stanford Sleep Clinic with PSGs containing\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory neural network was implemented to achieve a context-based mapping between the non-invasive features and the\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    .\n                    <jats:italic>Main results<\/jats:italic>\n                    : The median difference between the measured and predicted\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    was 0.61 cmH\n                    <jats:sub>2<\/jats:sub>\n                    O with an interquartile range (IQR) of 2.99 cmH\n                    <jats:sub>2<\/jats:sub>\n                    O and 5th and 95th percentiles of\u2009\u2009\u22125.85 cmH\n                    <jats:sub>2<\/jats:sub>\n                    O and 5.47 cmH\n                    <jats:sub>2<\/jats:sub>\n                    O, respectively. The model performed well when compared to actual esophageal pressure signal (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    <jats:sub>median<\/jats:sub>\n                    \u2009\u2009=\u2009\u20090.581,\n                    <jats:italic>p<\/jats:italic>\n                    \u200a\u2009\u2009=\u2009\u20090.01; IQR\u2009\u2009=\u2009\u20090.298;\n                    <jats:italic>\u03c1<\/jats:italic>\n                    <jats:sub>5%<\/jats:sub>\n                    \u2009\u2009=\u2009\u20090.106;\n                    <jats:italic>\u03c1<\/jats:italic>\n                    <jats:sub>95%<\/jats:sub>\n                    \u2009\u2009=\u2009\u20090.843).\n                    <jats:italic>Significance<\/jats:italic>\n                    : A significant difference in predicted\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>es<\/jats:sub>\n                    , improving characterization of SDB events as obstructive or not.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ab0559","type":"journal-article","created":{"date-parts":[[2019,2,8]],"date-time":"2019-02-08T18:04:38Z","timestamp":1549649078000},"page":"025008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology*"],"prefix":"10.1088","volume":"40","author":[{"given":"Umaer","family":"Hanif","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8729-5224","authenticated-orcid":false,"given":"Logan D","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lotte","family":"Trap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eileen B","family":"Leary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"IV","given":"Hyatt","family":"Moore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Guilleminault","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Poul","family":"Jennum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helge B D","family":"Sorensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel J M","family":"Mignot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2019,2,26]]},"reference":[{"key":"pmeaab0559bib001","doi-asserted-by":"publisher","first-page":"17","DOI":"10.5665\/sleep.1580","type":"journal-article","article-title":"The treatment of central sleep apnea syndromes in adults: practice parameters with an evidence-based literature review and meta-analyses","volume":"35","author":"Aurora","year":"2012","journal-title":"Sleep"},{"key":"pmeaab0559bib002","author":"Berry","year":"2018","type":"book"},{"key":"pmeaab0559bib003","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.5664\/jcsm.6122","type":"journal-article","article-title":"Use of chest wall electromyography to detect respiratory effort during polysomnography","volume":"12","author":"Berry","year":"2016","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2018-11-10","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-02-08","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-02-26","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}