Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases
Abstract Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tag...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87634-z |
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| author | K. A. Muthukumar Dhruva Nandi Priya Ranjan Krithika Ramachandran Shiny PJ Anirban Ghosh Ashwini M Aiswaryah Radhakrishnan V. E. Dhandapani Rajiv Janardhanan |
| author_facet | K. A. Muthukumar Dhruva Nandi Priya Ranjan Krithika Ramachandran Shiny PJ Anirban Ghosh Ashwini M Aiswaryah Radhakrishnan V. E. Dhandapani Rajiv Janardhanan |
| author_sort | K. A. Muthukumar |
| collection | DOAJ |
| description | Abstract Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover’s Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT’s spectral insights and EMD’s capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84%, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large. |
| format | Article |
| id | doaj-art-bd13693788284904bef8d398cbc2b5c6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bd13693788284904bef8d398cbc2b5c62025-02-09T12:36:45ZengNature PortfolioScientific Reports2045-23222025-02-0115111910.1038/s41598-025-87634-zIntegrating electrocardiogram and fundus images for early detection of cardiovascular diseasesK. A. Muthukumar0Dhruva Nandi1Priya Ranjan2Krithika Ramachandran3Shiny PJ4Anirban Ghosh5Ashwini M6Aiswaryah Radhakrishnan7V. E. Dhandapani8Rajiv Janardhanan9University of Petroleum and Energy StudiesFaculty of Medicine and Health Sciences , SRM Medical College Hospital and Research Centre, SRM ISTUniversity of Petroleum and Energy StudiesCentre for High Impact Neuroscience and Translational Applications, TCG CrestFaculty of Medicine and Health Sciences , SRM Medical College Hospital and Research Centre, SRM ISTDepartment of Electronics and Communication, SRM University APAshwini Eye CareFaculty of Medicine and Health Sciences , SRM Medical College Hospital and Research Centre, SRM ISTSri Kalpana Heart CareFaculty of Medicine and Health Sciences , SRM Medical College Hospital and Research Centre, SRM ISTAbstract Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover’s Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT’s spectral insights and EMD’s capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84%, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large.https://doi.org/10.1038/s41598-025-87634-zEMDFundus imageCNNCVD prediction |
| spellingShingle | K. A. Muthukumar Dhruva Nandi Priya Ranjan Krithika Ramachandran Shiny PJ Anirban Ghosh Ashwini M Aiswaryah Radhakrishnan V. E. Dhandapani Rajiv Janardhanan Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases Scientific Reports EMD Fundus image CNN CVD prediction |
| title | Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| title_full | Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| title_fullStr | Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| title_full_unstemmed | Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| title_short | Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| title_sort | integrating electrocardiogram and fundus images for early detection of cardiovascular diseases |
| topic | EMD Fundus image CNN CVD prediction |
| url | https://doi.org/10.1038/s41598-025-87634-z |
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