A machine learning approach to assess magnitude of asynchrony breathing

• Main Authors: Loo, Nienloong, Chiew, Yeong Shiong, Tan, Chee Pin, Mat Nor, Mohd Basri, Md Ralib, Azrina
• Format: Article
• Language: English; English
• Published: Elsevier Ltd. 2021
• Subjects: RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
• Online Access: http://irep.iium.edu.my/89103/7/89103_A%20machine%20learning%20approach%20to%20assess%20magnitude%20of%20asynchrony%20breathing%20-%20Loo.pdf; http://irep.iium.edu.my/89103/8/89103_Scopus%20-%20A%20machine%20learning%20approach%20to.pdf; http://irep.iium.edu.my/89103/; https://www.sciencedirect.com/science/article/abs/pii/S1746809421001026?via%3Dihub

Background: Conventional patient-ventilator interaction (PVI) assessment involves manual asynchronous index (AI) computation and incapable to provide in-depth information of the severity of asynchrony breathing (AB) during mechanical ventilation (MV). In this study, a novel convolutional autoencod...