Print Email Facebook Twitter Automated Detection of Patient-Ventilator Asynchrony in Mechanically Ventilated Patients Title Automated Detection of Patient-Ventilator Asynchrony in Mechanically Ventilated Patients Author de Bie, Melissa (TU Delft Mechanical Engineering; Leiden University Medical Center) Contributor Tax, D.M.J. (mentor) Schoe, A. (mentor) van Westerloo, D. (graduation committee) Degree granting institution Delft University of Technology Programme Technical Medicine | Sensing and Stimulation Project TM30004; 35 ECTS Date 2024-04-12 Abstract IntroductionPatient-ventilator asynchrony (PVA) poses a significant challenge in the management of mechanically ventilated patients, contributing to adverse clinical outcomes. Current methods of detecting PVA rely on visual assessment by clinicians, leading to subjectivity and inconsistency. Therefore, there is a need for automated techniques to identify PVA accurately and efficiently. In this study, we explore the application of supervised and unsupervised machine learning algorithms to develop an automatic detection system for PVA.MethodsThis study was conducted at the ICU of the LUMC in Leiden, the Netherlands. Patients eligible for inclusion were mechanically ventilated with an esophageal balloon inserted. Data collected included flow, Paw, and Pes curves, which were labelled using an open-source data labeling platform and processed in Python. Supervised CNN models were trained for different ventilation modes, while unsupervised techniques, utilizing Mahalanobis distance, were explored for data pre-labeling. The discriminative capability of the models was assessed using AUROC values.Results25 patients were included in this study and labelled by clinicians. Using an unsupervised machine learning technique based on the Mahalanobis distance for data pre-labeling, a threshold of 3.5 was selected, resulting in a 95% accuracy in correctly identifying normal breaths. Creating different CNN models for automating the detection of PVA the results demonstrate the discriminative capability of the various models across all ventilation modes, PSV and PCV ventilation. They can differentiate between normal and abnormal breaths, as indicated by the AUROC values of 0.85(±0.08), 0.83 (±0.12), and 0.80 (±0.28) respectively.DiscussionThis study investigated the application of machine learning techniques to analyse ventilation data in critical care settings. Through a combination of unsupervised and supervised learning methods, we have explored the automation of the labelling process and the development of predictive models for identifying patient-ventilator asynchronies (PVAs). Subject Patient-ventilator asynchronyMachine learningIntensive Care Unit (ICU)Mechanical Ventilation To reference this document use: http://resolver.tudelft.nl/uuid:27cba232-5706-4cb0-ae0f-75c0105eeada Part of collection Student theses Document type master thesis Rights © 2024 Melissa de Bie Files PDF Thesis_TM_PVA_Melissa_de_Bie_.pdf 3.34 MB Close viewer /islandora/object/uuid:27cba232-5706-4cb0-ae0f-75c0105eeada/datastream/OBJ/view