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A machine learning approach to assess magnitude of asynchrony breathing

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...

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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
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https://www.sciencedirect.com/science/article/abs/pii/S1746809421001026?via%3Dihub
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spelling my.iium.irep.891032021-03-31T08:00:54Z http://irep.iium.edu.my/89103/ A machine learning approach to assess magnitude of asynchrony breathing Loo, Nienloong Chiew, Yeong Shiong Tan, Chee Pin Mat Nor, Mohd Basri Md Ralib, Azrina RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid 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 autoencoder model (ABReCA) is developed to quantify the magnitude of AB as indicator of PVI. Method: ABReCA was trained with 400.000 unique AB to recognise its corresponding normal breathing (NB) cycle. The model then quantifies the severity of AB through comparison between identified NB waveform and AB. The magnitude of asynchrony (Masyn) is defined as the difference of a NB cycle affected by asynchronous patient’s effort. The performance of ABReCA was evaluated using K-folds analysis and used to measure the severity of AB in 10 mechanical ventilated respiratory failure patients. Results: K-fold analysis showed thatABReCA achieved high performance with only median 0.008 [Interquartile range (IQR): 0.007− 0.010] validation error. The model was able to recognise AB and its corresponding NB cycle. For the actual MV patient analysis, a typical AI counter shows a median of 32.7 % [IQR: 32.1–34.4] per patient. However, in our magnitude analysis, these patients experienced Masyn with mean of 3.8 % [IQR: 1.7 %–4.6 %]. The severity result is significantly lower compared to counting numbers alone as some AB are negligible while others have more impact towards the overall MV delivery. Conclusion: A novelABReCA is developed and capable of quantifying the severity of AB during MV. This model can potentially provide a better indication of the severity of AB and better reflection of the quality of PVI. Elsevier Ltd. 2021-02-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/89103/7/89103_A%20machine%20learning%20approach%20to%20assess%20magnitude%20of%20asynchrony%20breathing%20-%20Loo.pdf application/pdf en http://irep.iium.edu.my/89103/8/89103_Scopus%20-%20A%20machine%20learning%20approach%20to.pdf Loo, Nienloong and Chiew, Yeong Shiong and Tan, Chee Pin and Mat Nor, Mohd Basri and Md Ralib, Azrina (2021) A machine learning approach to assess magnitude of asynchrony breathing. Biomedical Signal Processing and Control, 66. pp. 1-9. ISSN 1746-8094 E-ISSN 1746-8094 https://www.sciencedirect.com/science/article/abs/pii/S1746809421001026?via%3Dihub 10.1016/j.bspc.2021.102505
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
spellingShingle RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
Loo, Nienloong
Chiew, Yeong Shiong
Tan, Chee Pin
Mat Nor, Mohd Basri
Md Ralib, Azrina
A machine learning approach to assess magnitude of asynchrony breathing
description 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 autoencoder model (ABReCA) is developed to quantify the magnitude of AB as indicator of PVI. Method: ABReCA was trained with 400.000 unique AB to recognise its corresponding normal breathing (NB) cycle. The model then quantifies the severity of AB through comparison between identified NB waveform and AB. The magnitude of asynchrony (Masyn) is defined as the difference of a NB cycle affected by asynchronous patient’s effort. The performance of ABReCA was evaluated using K-folds analysis and used to measure the severity of AB in 10 mechanical ventilated respiratory failure patients. Results: K-fold analysis showed thatABReCA achieved high performance with only median 0.008 [Interquartile range (IQR): 0.007− 0.010] validation error. The model was able to recognise AB and its corresponding NB cycle. For the actual MV patient analysis, a typical AI counter shows a median of 32.7 % [IQR: 32.1–34.4] per patient. However, in our magnitude analysis, these patients experienced Masyn with mean of 3.8 % [IQR: 1.7 %–4.6 %]. The severity result is significantly lower compared to counting numbers alone as some AB are negligible while others have more impact towards the overall MV delivery. Conclusion: A novelABReCA is developed and capable of quantifying the severity of AB during MV. This model can potentially provide a better indication of the severity of AB and better reflection of the quality of PVI.
format Article
author Loo, Nienloong
Chiew, Yeong Shiong
Tan, Chee Pin
Mat Nor, Mohd Basri
Md Ralib, Azrina
author_facet Loo, Nienloong
Chiew, Yeong Shiong
Tan, Chee Pin
Mat Nor, Mohd Basri
Md Ralib, Azrina
author_sort Loo, Nienloong
title A machine learning approach to assess magnitude of asynchrony breathing
title_short A machine learning approach to assess magnitude of asynchrony breathing
title_full A machine learning approach to assess magnitude of asynchrony breathing
title_fullStr A machine learning approach to assess magnitude of asynchrony breathing
title_full_unstemmed A machine learning approach to assess magnitude of asynchrony breathing
title_sort machine learning approach to assess magnitude of asynchrony breathing
publisher Elsevier Ltd.
publishDate 2021
url 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
_version_ 1696976089616744448
score 13.211869

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