SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
Patient-ventilator asynchrony (PVA) is a common and critical issue during
mechanical ventilation, affecting up to 85% of patients. PVA can result in
clinical complications such as discomfort, sleep disruption, and potentially
more severe conditions like ventilator-induced lung injury and diaphragm
dysfunction. Traditional PVA management, which relies on manual adjustments by
healthcare providers, is often inadequate due to delays and errors. While
various computational methods, including rule-based, statistical, and deep
learning approaches, have been developed to detect PVA events, they face
challenges related to dataset imbalances and lack of interpretability. In this
work, we propose a shapelet-based approach SHIP for PVA detection, utilizing
shapelets - discriminative subsequences in time-series data - to enhance
detection accuracy and interpretability. Our method addresses dataset
imbalances through shapelet-based data augmentation and constructs a shapelet
pool to transform the dataset for more effective classification. The combined
shapelet and statistical features are then used in a classifier to identify PVA
events. Experimental results on medical datasets show that SHIP significantly
improves PVA detection while providing interpretable insights into model
decisions.
