Breathing Cycle-Aware Segmentation for Patient-Ventilator Asynchrony Detection

Patient-ventilator asynchrony (PVA) is a significant challenge in mechanical ventilation, affecting approximately 25% of intensive care unit patients and increasing the risk of lung and diaphragm injury. Segmenting breathing cycles from long ventilation waveforms is essential for the reliable detection of PVA events. However, existing segmentation methods present several limitations: manual annotation is time-consuming; fixed-length window and rule-based segmentation methods lack adaptability to varying respiratory patterns; and supervised deep learning (DL) segmentation methods require large amounts of labelled data for training. To address these issues, we propose an unsupervised breathing cycle-aware segmentation method tailored for PVA detection. Leveraging the quasi-periodic nature of ventilation waveforms, the proposed segmentation method integrates frequency-adaptive clustering, periodicity hints validation, and dynamic segmentation to identify breathing cycle boundaries. We evaluate the proposed breathing cycle-aware segmentation method on a real-world dataset from Austin Health, Melbourne, Australia, where it outperforms baseline approaches on five out of six evaluation metrics. Furthermore, classification experiments using two state-of-the-art DL-based classification models confirm that accurate segmentation of breathing cycles enhances PVA detection performance. In the future, the proposed breathing cycle-aware segmentation method could be integrated into ventilation systems to support clinical decision-making and improve patient care.