{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Obeso I"],"funding":["L. K. Whittier Foundation","NHLBI NIH HHS","National Heart, Lung, and Blood Institute","National Institutes of Health"],"pagination":["105251"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10426752"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["86(Pt C)"],"pubmed_abstract":["Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respirator"],"journal":["Biomedical signal processing and control"],"pubmed_title":["A Novel Application of Spectrograms with Machine Learning Can Detect Patient Ventilator Dyssynchrony."],"pmcid":["PMC10426752"],"funding_grant_id":["R01 HL164397","R01 HL134666","RO11HL124666"],"pubmed_authors":["Aczon M","Zhou A","Yoon B","Ledbetter D","Eckberg RA","Laksana E","Wetzel R","Mertan K","Obeso I","Khemani RG"],"additional_accession":[]},"is_claimable":false,"name":"A Novel Application of Spectrograms with Machine Learning Can Detect Patient Ventilator Dyssynchrony.","description":"Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respirator","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Sep","modification":"2026-06-03T00:37:35.736Z","creation":"2025-04-05T18:37:01.505Z"},"accession":"S-EPMC10426752","cross_references":{"pubmed":["37587924"],"doi":["10.1016/j.bspc.2023.105251"]}}