A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals
Abstract Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced data...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81992-w |
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| author | Md. Alamin Talukder Majdi Khalid Mohsin Kazi Nusrat Jahan Muna Mohammad Nur-e-Alam Sajal Halder Nasrin Sultana |
| author_facet | Md. Alamin Talukder Majdi Khalid Mohsin Kazi Nusrat Jahan Muna Mohammad Nur-e-Alam Sajal Halder Nasrin Sultana |
| author_sort | Md. Alamin Talukder |
| collection | DOAJ |
| description | Abstract Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants—ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall—combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare. |
| format | Article |
| id | doaj-art-f3656202e3c04abfae09264d95b1cb75 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f3656202e3c04abfae09264d95b1cb752024-12-08T12:24:38ZengNature PortfolioScientific Reports2045-23222024-12-0114112010.1038/s41598-024-81992-wA hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signalsMd. Alamin Talukder0Majdi Khalid1Mohsin Kazi2Nusrat Jahan Muna3Mohammad Nur-e-Alam4Sajal Halder5Nasrin Sultana6Department of Computer Science and Engineering, International University of Business Agriculture and TechnologyDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Computer Science and Engineering, Jagannath UniversityDepartment of Pharmacognosy, College of Pharmacy, King Saud UniversityData61, CSIRODepartment of ICT, Future Technology, RMIT UniversityAbstract Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants—ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall—combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare.https://doi.org/10.1038/s41598-024-81992-wArrhythmia detectionConvNeXt modelsData balancing techniquesSMOTE-TomekLinkDeep learning in healthcareElectrocardiogram classification |
| spellingShingle | Md. Alamin Talukder Majdi Khalid Mohsin Kazi Nusrat Jahan Muna Mohammad Nur-e-Alam Sajal Halder Nasrin Sultana A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals Scientific Reports Arrhythmia detection ConvNeXt models Data balancing techniques SMOTE-TomekLink Deep learning in healthcare Electrocardiogram classification |
| title | A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals |
| title_full | A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals |
| title_fullStr | A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals |
| title_full_unstemmed | A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals |
| title_short | A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals |
| title_sort | hybrid cardiovascular arrhythmia disease detection using convnext x models on electrocardiogram signals |
| topic | Arrhythmia detection ConvNeXt models Data balancing techniques SMOTE-TomekLink Deep learning in healthcare Electrocardiogram classification |
| url | https://doi.org/10.1038/s41598-024-81992-w |
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