Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series

Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer probabilities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce a generalised label-free framework, GenClean, for real-time artefact cleaning and leverage an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training. We first investigate patient-level generalisation, demonstrating robust performances under both intra- and inter-patient distribution shifts. We further validate its effectiveness through challenging cross-disease cohort experiments on the MIMIC-III database. Additionally, we extend our method to photoplethysmography (PPG), highlighting its applicability to diverse medical pulsatile signals. Finally, its integration into ICM+, a clinical research monitoring software, confirms the real-time feasibility of our framework, emphasising its practical utility in continuous physiological monitoring. This work provides a foundational step toward precision medicine in improving the reliability of high-resolution medical time series analysis

@article{chen2025_2504.21209,
  title={ Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series },
  author={ Xuhang Chen and Ihsane Olakorede and Stefan Yu Bögli and Wenhao Xu and Erta Beqiri and Xuemeng Li and Chenyu Tang and Zeyu Gao and Shuo Gao and Ari Ercole and Peter Smielewski },
  journal={arXiv preprint arXiv:2504.21209},
  year={ 2025 }
}