A machine learning model for real-time asynchronous breathing monitoring

• Main Authors: Loo, Nienloong, Chiew, Y. S., Tan, C. P., Arunachalam, Ganesaramachandran, Md Ralib, Azrina, Mat Nor, Mohd Basri
• Format: Article
• Language: English; English
• Published: Elsevier B.V. 2018
• Subjects: R Medicine (General); RC Internal medicine; RC803 Specialties of Internal Medicine-Diseases of The Digestive System. Gastroenterology; RD81 Anesthesiology
• Online Access: http://irep.iium.edu.my/70025/1/70025_A%20MACHINE%20LEARNING%20MODEL%20FOR%20REAL-TIME%20_article.pdf; http://irep.iium.edu.my/70025/2/70025_A%20MACHINE%20LEARNING%20MODEL%20FOR%20REAL-TIME%20_scopus.pdf; http://irep.iium.edu.my/70025/; https://reader.elsevier.com/reader/sd/pii/S2405896318333196?token=6A4211F58F309EFCB97F3CCDD97FD990729A8D8A00F3E9C219E972976D8AAC2FB4CCEADC4E650E2C68D6E8E822A41582
• Institution: Universiti Islam Antarabangsa Malaysia

The occurrence of asynchronous breathing (AB) during mechanical ventilation (MV) can have detrimental effect towards a patient's recovery. Hence, it is essential to develop an algorithm to automate AB detection in real-time. In this study, a method for AB detection using machine learning, in pa...