A Review on Automatic Detection Algorithm for Patient-Ventilator Asynchrony during Mechanical Ventilation
10.3969/j.issn.1671-7104.230209
- VernacularTitle:机械通气人机不同步自动检测算法综述
- Author:
Huaqing ZHANG
1
;
Lizhu WANG
;
Jianfeng XU
;
Yan XIANG
;
Zhaocai ZHANG
Author Information
1. 浙江大学医学院附属第二医院,杭州市,310009
- Keywords:
mechanical ventilation;
patient-ventilator asynchrony;
automatic detection;
algorithm
- From:
Chinese Journal of Medical Instrumentation
2024;48(1):44-50
- CountryChina
- Language:Chinese
-
Abstract:
This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony(PVA)during mechanical ventilation.In the early stages,the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms.While these methods were intuitive and easy to operate,they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status.Subsequently,machine learning and deep learning technologies began to emerge and develop.These technologies automatically extract and learn data characteristics through algorithms,making PVA detection more robust and universal.Among them,logistic regression,support vector machines,random forest,hidden Markov models,convolutional autoencoders,long short-term memory networks,one-dimensional convolutional neural networks,etc.,have all been successfully used for PVA recognition.Despite the significant advancements in feature extraction through deep learning methods,their demand for labelled data is high,potentially consuming significant medical resources.Therefore,the combination of reinforcement learning and self-supervised learning may be a viable solution.In addition,most algorithm validations are based on a single dataset,so the need for cross-dataset validation in the future will be an important and challenging direction for development.
