Robust screening of atrial fibrillation with distribution classification
Abstract Atrial fibrillation (AF) correlates with an increased risk of all-cause mortality or stroke, mainly due to undiagnosed patients and undertreatment. Its screening is thus a key challenge, for which machine learning methods hold the promise of cheaper and faster campaigns. The robustness of s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10090-2 |
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| author | Pierre-François Massiani Lukas Haverbeck Claas Thesing Friedrich Solowjow Marlo Verket Matthias Daniel Zink Katharina Schütt Dirk Müller-Wieland Nikolaus Marx Sebastian Trimpe |
| author_facet | Pierre-François Massiani Lukas Haverbeck Claas Thesing Friedrich Solowjow Marlo Verket Matthias Daniel Zink Katharina Schütt Dirk Müller-Wieland Nikolaus Marx Sebastian Trimpe |
| author_sort | Pierre-François Massiani |
| collection | DOAJ |
| description | Abstract Atrial fibrillation (AF) correlates with an increased risk of all-cause mortality or stroke, mainly due to undiagnosed patients and undertreatment. Its screening is thus a key challenge, for which machine learning methods hold the promise of cheaper and faster campaigns. The robustness of such methods to varying artifacts, noise, and conditions is then crucial. We introduce the first distributional support vector machine (SVM) for robust detection of AF from short, noisy electrocardiograms. It achieves state-of-the-art performance and unprecedented robustness on the screening problem while only leveraging one interpretable feature and little training data. We illustrate these advantages by evaluating on other data sources (cross-data-set) and through sensitivity studies. These strengths result from two main components: (i) preliminary peak detection enabling robust computation of medically relevant features; and (ii) a mathematically principled way of aggregating those features to compare their full distributions. This establishes our algorithm as a relevant candidate for screening campaigns. |
| format | Article |
| id | doaj-art-bd36ba6a3fc744a9a1642e3f4d88cc1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bd36ba6a3fc744a9a1642e3f4d88cc1f2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-011511910.1038/s41598-025-10090-2Robust screening of atrial fibrillation with distribution classificationPierre-François Massiani0Lukas Haverbeck1Claas Thesing2Friedrich Solowjow3Marlo Verket4Matthias Daniel Zink5Katharina Schütt6Dirk Müller-Wieland7Nikolaus Marx8Sebastian Trimpe9Institute for Data Science in Mechanical Engineering, RWTH Aachen UniversityInstitute for Data Science in Mechanical Engineering, RWTH Aachen UniversityInstitute for Data Science in Mechanical Engineering, RWTH Aachen UniversityInstitute for Data Science in Mechanical Engineering, RWTH Aachen UniversityDepartment of Internal Medicine I, University Hospital RWTH AachenDepartment of Internal Medicine I, University Hospital RWTH AachenDepartment of Internal Medicine I, University Hospital RWTH AachenDepartment of Internal Medicine I, University Hospital RWTH AachenDepartment of Internal Medicine I, University Hospital RWTH AachenInstitute for Data Science in Mechanical Engineering, RWTH Aachen UniversityAbstract Atrial fibrillation (AF) correlates with an increased risk of all-cause mortality or stroke, mainly due to undiagnosed patients and undertreatment. Its screening is thus a key challenge, for which machine learning methods hold the promise of cheaper and faster campaigns. The robustness of such methods to varying artifacts, noise, and conditions is then crucial. We introduce the first distributional support vector machine (SVM) for robust detection of AF from short, noisy electrocardiograms. It achieves state-of-the-art performance and unprecedented robustness on the screening problem while only leveraging one interpretable feature and little training data. We illustrate these advantages by evaluating on other data sources (cross-data-set) and through sensitivity studies. These strengths result from two main components: (i) preliminary peak detection enabling robust computation of medically relevant features; and (ii) a mathematically principled way of aggregating those features to compare their full distributions. This establishes our algorithm as a relevant candidate for screening campaigns.https://doi.org/10.1038/s41598-025-10090-2Atrial fibrillationScreeningSupport vector machinesDistribution classificationMachine learning |
| spellingShingle | Pierre-François Massiani Lukas Haverbeck Claas Thesing Friedrich Solowjow Marlo Verket Matthias Daniel Zink Katharina Schütt Dirk Müller-Wieland Nikolaus Marx Sebastian Trimpe Robust screening of atrial fibrillation with distribution classification Scientific Reports Atrial fibrillation Screening Support vector machines Distribution classification Machine learning |
| title | Robust screening of atrial fibrillation with distribution classification |
| title_full | Robust screening of atrial fibrillation with distribution classification |
| title_fullStr | Robust screening of atrial fibrillation with distribution classification |
| title_full_unstemmed | Robust screening of atrial fibrillation with distribution classification |
| title_short | Robust screening of atrial fibrillation with distribution classification |
| title_sort | robust screening of atrial fibrillation with distribution classification |
| topic | Atrial fibrillation Screening Support vector machines Distribution classification Machine learning |
| url | https://doi.org/10.1038/s41598-025-10090-2 |
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