Abstract
Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer opportunities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce GenClean, a generalised label-free framework for real-time artefact cleaning, implemented within the ICM+ clinical research monitoring software. Leveraging an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training, we first investigate patient-level generalisation, demonstrating robust performance under both intra- and inter-patient distribution shifts. As an initial exploration beyond the development cohort, we further validate its effectiveness for ABP through site-level generalisation on the MIMIC-III database. We also provided an extension of our method to photoplethysmography (PPG), highlighting its potential applicability to diverse medical pulsatile signals. The real-time integration and these generalisation studies collectively demonstrate the practical utility of our framework in continuous physiological monitoring and represent a promising step towards improving the reliability of high-resolution medical time series analysis.