Effects of data structure in convolutional neural network for detection of asynchronous breathing in mechanical ventilation treatment

Christopher Yew Shuen Ang, Nien Loong Loo, Yeong Shiong Chiew, Chee Pin Tan, Mohd Basri Mat Nor, J. Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


Asynchronous breathing (AB) in mechanical ventilation (MV) patients is heterogenous, patient-specific, and is associated with adverse patient outcomes. Various machine learning models have been developed for AB detection, however studies regarding the data structures used for model training are scarce. This study investigates the effects of different training data structures and sizes of Convolutional Neural Networks (CNN) to detect AB. Four CNN models were developed using different amounts of data and data structures: one-dimension, line, area, and array. Training datasets consisting of 300, 1,000, 5,000 and 10,000 airway pressure waveforms from MV patients were used for model development. Model sensitivity and specificity were evaluated using an independent set of 3000 waveforms in a 100-iteration Monte-Carlo analysis. The best-performing CNN model was used to determine Asynchrony Index (AI) values in a clinical patient cohort. Monte-Carlo analysis showed that models trained with datasets of 10,000 breathing cycles delivered $\gt99$% sensitivity and specificity. Relatively lower sensitivity and specificity of $\lt78.8$% and $\lt96.7$% respectively were obtained when trained with data quantities of 5000 breaths or less. A CNN trained with 1Dimensional data structure yielded 99.9% sensitivity and 99.6% specificity. It achieved 88.5% average accuracy when validated with an independent clinical data set of 544,319 breaths. Asynchrony breathing detection is ubiquitous and 1-Dimensional data structures provide a resource efficient method for the development of an accurate CNN model.

Original languageEnglish
Title of host publication7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665494694
Publication statusPublished - 2022
EventIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2022 - Online, Malaysia
Duration: 7 Dec 20229 Dec 2022
Conference number: 7th
https://ieeexplore.ieee.org/xpl/conhome/10079231/proceeding (Proceedings)
https://www.iecbes.org/ (Website)


ConferenceIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2022
Abbreviated titleIECBES 2022
Internet address


  • This research develops Convolutional Neural Networks based on different data structures to detect asynchronous breathing in mechanical ventilation patients

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