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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.21209 |
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| _version_ | 1866913812896022528 |
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| author | Chen, Xuhang Olakorede, Ihsane Bögli, Stefan Yu Xu, Wenhao Beqiri, Erta Li, Xuemeng Tang, Chenyu Gao, Zeyu Gao, Shuo Ercole, Ari Smielewski, Peter |
| author_facet | Chen, Xuhang Olakorede, Ihsane Bögli, Stefan Yu Xu, Wenhao Beqiri, Erta Li, Xuemeng Tang, Chenyu Gao, Zeyu Gao, Shuo Ercole, Ari Smielewski, Peter |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series Chen, Xuhang Olakorede, Ihsane Bögli, Stefan Yu Xu, Wenhao Beqiri, Erta Li, Xuemeng Tang, Chenyu Gao, Zeyu Gao, Shuo Ercole, Ari Smielewski, Peter Signal Processing Machine Learning 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 |
| title | Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2504.21209 |