A novel application of spectrograms with machine learning can detect patient ventilator dyssynchrony-Reference-Cited by-同舟云学术

A novel application of spectrograms with machine learning can detect patient ventilator dyssynchrony

Published:2023-09 Issue: Volume:86 Page:105251
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Obeso Ishmael^ORCID,Yoon Benjamin^ORCID,Ledbetter David,Aczon Melissa^ORCID,Laksana Eugene,Zhou Alice^ORCID,Eckberg R. Andrew,Mertan Keith,Khemani Robinder G.^ORCID,Wetzel Randall^ORCID

Funder

National Institutes of Health

National Heart, Lung, and Blood Institute

L. K. Whittier Foundation

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference30 articles.

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3. Risk factors for patient–Ventilator asynchrony and its impact on clinical outcomes: Analytics based on deep learning algorithm;Ge;Front. Med.,2020

4. Predictors of asynchronies during assisted ventilation and its impact on clinical outcomes: The EPISYNC cohort study;de Araújo Sousa;J. Crit. Care,2020

5. Patient-ventilator Dyssynchrony;Antonogiannaki;Korean J. Crit. Care Med.,2017

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