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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, 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.