Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases

• URL: http://arxiv.org/abs/2504.10493v1
• Date: Mon, 31 Mar 2025 07:53:36 GMT
• Title: Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases
• Authors: K. A. Muthukumar, Dhruva Nandi, Priya Ranjan, Krithika Ramachandran, Shiny PJ, Anirban Ghosh, Ashwini M, Aiswaryah Radhakrishnan, V. E. Dhandapani, Rajiv Janardhanan,
• Abstract summary: Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques.<n>We present an avant-garde methodology that synergistically integrates ECG readings and fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority.<n>Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy.
• Score: 1.732458484303615
• License: http://creativecommons.org/licenses/by/4.0/
• 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 percent, 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.

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