Atrial fibrillation detection via contactless radio monitoring and knowledge transfer
Abstract Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogram (ECG) is considered as the golden standard for AF diagnosis. However, current ECG is primarily used only when symptoms arise or for occasio...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59482-y |
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| author | Yuqin Yuan Jinbo Chen Dongheng Zhang Ruixu Geng Hanqin Gong Guixin Xu Yu Pu Zhi Lu Yang Hu Dong Zhang Likun Ma Qibin Sun Yan Chen |
| author_facet | Yuqin Yuan Jinbo Chen Dongheng Zhang Ruixu Geng Hanqin Gong Guixin Xu Yu Pu Zhi Lu Yang Hu Dong Zhang Likun Ma Qibin Sun Yan Chen |
| author_sort | Yuqin Yuan |
| collection | DOAJ |
| description | Abstract Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogram (ECG) is considered as the golden standard for AF diagnosis. However, current ECG is primarily used only when symptoms arise or for occasional checkups due to the necessity of contact-based measurements. This limitation results in difficulty of capturing early-stage AF episodes and missed opportunities for timely intervention. Here we introduce a contactless, operation-free, and device-free AF detection framework utilizing artificial intelligence (AI)-powered radio technology. Our approach analyzes the mechanical motion of the heart using radar sensing and leverages AI-powered knowledge transfer from established clinical ECG diagnostic practices to read AF-associated motion patterns precisely. Our system is evaluated on 6258 outpatient visitors, including 229 with AF, and achieves AF detection with a sensitivity of 0.844 (95% Confidence Interval (CI), 0.790-0.884) and a specificity of 0.995 (95% CI, 0.993-0.997), which is comparable to the performance of ECG-based methods. We also provide initial evidence that this system could be deployed in a practical daily life scenario, detecting AF before traditional clinical diagnosis routines. These results highlight its potential to support feasible lifelong proactive monitoring, covering the full spectrum of AF progression. |
| format | Article |
| id | doaj-art-c1eb15d101ce402fa16169827ae8a506 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-c1eb15d101ce402fa16169827ae8a5062025-08-20T03:48:15ZengNature PortfolioNature Communications2041-17232025-05-0116111110.1038/s41467-025-59482-yAtrial fibrillation detection via contactless radio monitoring and knowledge transferYuqin Yuan0Jinbo Chen1Dongheng Zhang2Ruixu Geng3Hanqin Gong4Guixin Xu5Yu Pu6Zhi Lu7Yang Hu8Dong Zhang9Likun Ma10Qibin Sun11Yan Chen12Department of Cardiology, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Cyber Science and Technology, University of Science and Technology of ChinaSchool of Information Science and Technology, University of Science and Technology of ChinaZhongke Radio Sensing AI Technology Co., Ltd.Department of Cardiology, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaZhongke Radio Sensing AI Technology Co., Ltd.Department of Cardiology, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaAbstract Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogram (ECG) is considered as the golden standard for AF diagnosis. However, current ECG is primarily used only when symptoms arise or for occasional checkups due to the necessity of contact-based measurements. This limitation results in difficulty of capturing early-stage AF episodes and missed opportunities for timely intervention. Here we introduce a contactless, operation-free, and device-free AF detection framework utilizing artificial intelligence (AI)-powered radio technology. Our approach analyzes the mechanical motion of the heart using radar sensing and leverages AI-powered knowledge transfer from established clinical ECG diagnostic practices to read AF-associated motion patterns precisely. Our system is evaluated on 6258 outpatient visitors, including 229 with AF, and achieves AF detection with a sensitivity of 0.844 (95% Confidence Interval (CI), 0.790-0.884) and a specificity of 0.995 (95% CI, 0.993-0.997), which is comparable to the performance of ECG-based methods. We also provide initial evidence that this system could be deployed in a practical daily life scenario, detecting AF before traditional clinical diagnosis routines. These results highlight its potential to support feasible lifelong proactive monitoring, covering the full spectrum of AF progression.https://doi.org/10.1038/s41467-025-59482-y |
| spellingShingle | Yuqin Yuan Jinbo Chen Dongheng Zhang Ruixu Geng Hanqin Gong Guixin Xu Yu Pu Zhi Lu Yang Hu Dong Zhang Likun Ma Qibin Sun Yan Chen Atrial fibrillation detection via contactless radio monitoring and knowledge transfer Nature Communications |
| title | Atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| title_full | Atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| title_fullStr | Atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| title_full_unstemmed | Atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| title_short | Atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| title_sort | atrial fibrillation detection via contactless radio monitoring and knowledge transfer |
| url | https://doi.org/10.1038/s41467-025-59482-y |
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