Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism
Abstract Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-71366-7 |
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| author | Fayez Saud Alreshidi Mohammad Alsaffar Rajeswari Chengoden Naif Khalaf Alshammari |
| author_facet | Fayez Saud Alreshidi Mohammad Alsaffar Rajeswari Chengoden Naif Khalaf Alshammari |
| author_sort | Fayez Saud Alreshidi |
| collection | DOAJ |
| description | Abstract Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals. |
| format | Article |
| id | doaj-art-34a3ea93b74945f397d2f93e597254ce |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-34a3ea93b74945f397d2f93e597254ce2025-01-05T12:30:47ZengNature PortfolioScientific Reports2045-23222024-09-0114111910.1038/s41598-024-71366-7Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanismFayez Saud Alreshidi0Mohammad Alsaffar1Rajeswari Chengoden2Naif Khalaf Alshammari3Department of Family and Community Medicine, College of Medicine, University of Ha’ilDepartment of Computer Science and Software Engineering, College of Computer Science and Engineering, University of Ha’ilSchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyMechanical Engineering Department, Engineering College, University of Ha’ilAbstract Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.https://doi.org/10.1038/s41598-024-71366-7 |
| spellingShingle | Fayez Saud Alreshidi Mohammad Alsaffar Rajeswari Chengoden Naif Khalaf Alshammari Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism Scientific Reports |
| title | Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism |
| title_full | Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism |
| title_fullStr | Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism |
| title_full_unstemmed | Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism |
| title_short | Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism |
| title_sort | fed cl an atrial fibrillation prediction system using ecg signals employing federated learning mechanism |
| url | https://doi.org/10.1038/s41598-024-71366-7 |
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