Hierarchical Clustering with Dynamic Time Warping for Automatic Detection and Labeling of Triggering Asynchrony in Patient-Ventilator Interaction during Mechanical Ventilation

Wang Xuan, Wang Zunliang, Chen Cheng, Liu Songqiao
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:238-245, 2024.

Abstract

The patient-ventilator asynchronies in the ICU can significantly impact the management and prognosis of mechanical ventilation. Currently, machine learning methods have shown promise in ef- fectively identifying patient-ventilator asynchrony (PVA). However, there is still a lack of training datasets for PVA detection. In this study, a hierarchical clustering with dynamic time warping (DTW) method is presented to perform automatic identification and labeling of trigger asynchrony waveforms within our abnormal breathing cycle dataset for automatic identification and labeling of abnormal waveform datasets in mechanical ventilation, thereby reducing the workload of hand labeling. The experimental results show that our method can efficiently identify both ineffective triggering and double triggering from a large amount of abnormal ventilation data. These two types of trigger abnormalities are widely observed in patient-ventilator asynchronies (PVAs) during mechanical ventilation. Automatically identifying and annotating these abnormalities is crucial for reducing the burden of manual labeling, promoting the creation of PVA training datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-xuan24a, title = {Hierarchical Clustering with Dynamic Time Warping for Automatic Detection and Labeling of Triggering Asynchrony in Patient-Ventilator Interaction during Mechanical Ventilation}, author = {Xuan, Wang and Zunliang, Wang and Cheng, Chen and Songqiao, Liu}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {238--245}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/xuan24a/xuan24a.pdf}, url = {https://proceedings.mlr.press/v245/xuan24a.html}, abstract = {The patient-ventilator asynchronies in the ICU can significantly impact the management and prognosis of mechanical ventilation. Currently, machine learning methods have shown promise in ef- fectively identifying patient-ventilator asynchrony (PVA). However, there is still a lack of training datasets for PVA detection. In this study, a hierarchical clustering with dynamic time warping (DTW) method is presented to perform automatic identification and labeling of trigger asynchrony waveforms within our abnormal breathing cycle dataset for automatic identification and labeling of abnormal waveform datasets in mechanical ventilation, thereby reducing the workload of hand labeling. The experimental results show that our method can efficiently identify both ineffective triggering and double triggering from a large amount of abnormal ventilation data. These two types of trigger abnormalities are widely observed in patient-ventilator asynchronies (PVAs) during mechanical ventilation. Automatically identifying and annotating these abnormalities is crucial for reducing the burden of manual labeling, promoting the creation of PVA training datasets.} }
Endnote
%0 Conference Paper %T Hierarchical Clustering with Dynamic Time Warping for Automatic Detection and Labeling of Triggering Asynchrony in Patient-Ventilator Interaction during Mechanical Ventilation %A Wang Xuan %A Wang Zunliang %A Chen Cheng %A Liu Songqiao %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-xuan24a %I PMLR %P 238--245 %U https://proceedings.mlr.press/v245/xuan24a.html %V 245 %X The patient-ventilator asynchronies in the ICU can significantly impact the management and prognosis of mechanical ventilation. Currently, machine learning methods have shown promise in ef- fectively identifying patient-ventilator asynchrony (PVA). However, there is still a lack of training datasets for PVA detection. In this study, a hierarchical clustering with dynamic time warping (DTW) method is presented to perform automatic identification and labeling of trigger asynchrony waveforms within our abnormal breathing cycle dataset for automatic identification and labeling of abnormal waveform datasets in mechanical ventilation, thereby reducing the workload of hand labeling. The experimental results show that our method can efficiently identify both ineffective triggering and double triggering from a large amount of abnormal ventilation data. These two types of trigger abnormalities are widely observed in patient-ventilator asynchronies (PVAs) during mechanical ventilation. Automatically identifying and annotating these abnormalities is crucial for reducing the burden of manual labeling, promoting the creation of PVA training datasets.
APA
Xuan, W., Zunliang, W., Cheng, C. & Songqiao, L.. (2024). Hierarchical Clustering with Dynamic Time Warping for Automatic Detection and Labeling of Triggering Asynchrony in Patient-Ventilator Interaction during Mechanical Ventilation. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:238-245 Available from https://proceedings.mlr.press/v245/xuan24a.html.

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