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A machine learning model for real-time asynchronous breathing monitoring

Loo, Nienloong and Chiew, Y. S. and Tan, C. P. and Arunachalam, Ganesaramachandran and Md Ralib, Azrina and Mat Nor, Mohd Basri (2018) A machine learning model for real-time asynchronous breathing monitoring. IFAC-PapersOnLine, 51 (27). pp. 378-383. ISSN 2405-8963

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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 particular, Convolutional Neural Network, (CNN), is presented and its performance in identifying AB when trained with different amount of training datasets and different types of training datasets is evaluated and compared between standard manual detection. A total of 486,200 breaths were analyzed in this study. It was found that the CNN algorithm achieved 69.4% sensitivity and 37.1% specificity when trained with 2000 AB cycles and 1000 normal breathing (NB) cycles; however, when it was trained with 5500 AB and 5500 NB, the CNN achieved 96.9% sensitivity and 63.7% specificity. The experimental results also indicate that the CNN was trained with modified images (region under the curve) CNN yielded sensitivity of 98.5% and specificity of 89.4% as opposed to sensitivity of 25.3% and 83.9% specificity when trained with line graph instead. Therefore the proposed method can potentially provide real-time assessment and information for the clinicians.

Item Type: Article (Journal)
Additional Information: 3934/70025
Uncontrolled Keywords: Asynchronous breathing (AB); Convolutional neural networks (CNN); Machine learning
Subjects: R Medicine > R Medicine (General)
R Medicine > RC Internal medicine
R Medicine > RC Internal medicine > RC803 Specialties of Internal Medicine-Diseases of The Digestive System. Gastroenterology
R Medicine > RD Surgery > RD81 Anesthesiology
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Medicine
Kulliyyah of Medicine > Department of Anaesthesiology & Intensive Care
Depositing User: Prof Azrina Md Ralib
Date Deposited: 23 Jan 2019 15:37
Last Modified: 23 Jan 2019 15:37
URI: http://irep.iium.edu.my/id/eprint/70025

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