Volume: 2 Issue: 1
Year: 2025, Page: 12-22, Doi: https://doi.org/10.70968/ijeaca.v2i1.D1012
Received: Feb. 10, 2025 Accepted: June 25, 2025 Published: June 30, 2025
Millions of people throughout the world suffer with diabetes mellitus, a common chronic illness. Reducing the chance of serious consequences requires prompt diagnosis and efficient treatment. This paper presents a novel approach to real-time diabetes monitoring by combining deep neural networks with electrocardiogram (ECG) signal analysis. ECG is a non-invasive technique that captures the electrical activity of the heart, which can indicate physiological alterations associated with diabetes. To accurately classify people as diabetes or non-diabetic, a deep learning model was created to effectively extract important elements from ECG data. A large dataset of ECG recordings from both groups was used to train the model. A safe and effective data transfer architecture enables real-time application, making it simple for users to view their diabetes status. The results validate the accuracy and feasibility of using deep learning with ECG for diabetes monitoring, offering a useful tool for improving diabetes management by giving patients and healthcare professionals timely information.
Keywords: Neural Network Based Diabetes Detection Using ECG Signals
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© 2025 Shringarpure et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Shringarpure V, Shivshankar G, More P, Chavan PM, Joshi KR. (2025). Neural Network Based Diabetes Detection Using ECG Signals. International Journal of Electronics and Computer Applications. 2(1): 12-22. https://doi.org/10.70968/ijeaca.v2i1.D1012