Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients

Muhamad Sauki, Nur Sa’adah and Damanhuri, Nor Salwa and Othman, Nor Azlan and Yeong, Shiong Chiew and Chong, Belinda Chiew Meng and Mat Nor, Mohd Basri and Chase, J․Geoffrey (2025) Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients. Computer Methods and Programs in Biomedicine, 263. pp. 1-11. ISSN 0169-2607 E-ISSN 1872-7565

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Abstract

Background and objective: Asynchronous breathing (AB) occurs when a mechanically ventilated patient’s breathing does not align with the mechanical ventilator (MV). Asynchrony can negatively impact recovery and outcome, and/or hinder MV management. A model-based method to accurately classify different AB types could automate detection and have a measurable clinical impact. Methods: This study presents an approach using a 1-dimensional (1D) of airway pressure data as an input to the convolutional long short-term memory neural network (CNN-LSTM) with a classifier method to classify AB types into three categories: 1) reverse Triggering (RT); 2) premature cycling (PC); and 3) normal breathing (NB), which cover normal breathing and 2 primary forms of AB. Three types of classifier are integrated with the CNN-LSTM model which are random forest (RF), support vector machine (SVM) and logistic regression (LR). Clinical data inputs include measured airway pressure from 7 MV patients in IIUM Hospital ICU under informed consent with a total of 4500 breaths. Model performance is first assessed in a k-fold cross-validation assessing accuracy in comparison to the proposed CNN-LSTM integrated with each type of classifier. Then, confusion matrices are used to summarize classification performance for the CNN without classifier, CNN-LSTM without classifier, and CNN-LSTM with each of the 3 classifiers (RF, SVM, LR). Results and discussion: The 1D CNN-LSTM with classifier method achieves 100 % accuracy using 5-fold cross validation. The confusion matrix results showed that the combined CNN-LSTM model with classifier performed better, demostrating higher accuracy, sensitivity, specificity, and F1 score, all exceeding 83.5 % across all three breathing categories. The CNN model without classifier and CNN-LSTM model without classifier displayed comparatively lower performance, with average values of F1 score below 71.8 % for all three breathing categories. Conclusion: The results validate the effectiveness of the CNN-LSTM neural network model with classifier in accurately detecting and classifying the different categories of AB and NB. Overall, this model-based approach has the potential to precisely classify the type of AB and differentiate normal breathing. With this developed model, a better MV management can be provided at the bedside, and these results justify prospective clinical testing.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Mechanical ventilation Asynchrony breathing Convolutional neural network (CNN) Long short-term memory neural network (LSTM) Classifier Respiratory mechanics
Subjects:	R Medicine > RC Internal medicine > RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering
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:	Dr. Mohd Basri Mat Nor
Date Deposited:	02 Oct 2025 14:30
Last Modified:	02 Oct 2025 14:30
URI:	http://irep.iium.edu.my/id/eprint/123474

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