Reducing annotation effort in patient-ventilator asynchrony detection with distance-based clustering

Abstract

Objective: This study aims to reduce expert annotation effort in detecting patient-ventilator asynchrony (PVA) by introducing a semi-supervised learning framework for time series classification. Methods and procedures: We propose a model-independent framework that integrates hierarchical clustering and dynamic time-warping (DTW) for efficient data selection and label projection. The framework includes five steps: data collection, selection, annotation, projection, and model training. It is validated using a fully labeled dataset from Fondazione I.R.C.C.S. Policlinico San Matteo and applied to an unlabeled dataset from Maasstad Hospital, where annotation consistency and label quality are analyzed. Results: The framework reduces annotation effort by over 75% while closely resembling classification performance. On the San Matteo dataset, the model trained with projected labels achieved performance close to that of a fully supervised model. The method effectively captured rare PVA types and improved macro-averaged F1 scores compared to random sampling. On the Maasstad dataset, despite annotation inconsistencies, the framework demonstrated moderate detection performance (75% micro-averaged F₁ score) using labels from a single clinical expert. Conclusion: Our semi-supervised framework enables scalable and efficient annotation of clinical time series data, maintaining model accuracy with minimal expert input. It is robust across datasets and adaptable to varying signal quality and annotation consistency.