Published on: April 2026
AUTOMATED HEART DISEASE DETECTION FROM ECG SIGNALS USING A HYBRID DEEP LEARNING APPROACH
Gatta Midhun Kumar
Dr. Vanitha Kakollu
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Abstract
Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and accurate diagnosis critically important. Electrocardiogram (ECG) signals are widely used for detecting heart-related abnormalities; however, manual interpretation is time- consuming and prone to human error. This research proposes an automated ECG classification system using advanced deep learning models including Convolutional Neural Networks (CNN), MobileNet, DenseNet, and a hybrid MobileNet + LSTM model. The system classifies ECG images into four categories: myocardial infarction, history of myocardial infarction, abnormal heartbeat, and normal heart conditions. Experimental results indicate that the hybrid model outperforms individual models by effectively capturing both spatial and temporal features. The proposed system demonstrates high accuracy and reliability, making it suitable for real- world clinical applications.
How to Cite this Paper
Kumar, G. M. (2026). Automated Heart Disease Detection from ECG Signals using a Hybrid Deep Learning Approach. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.488
Kumar, Gatta. "Automated Heart Disease Detection from ECG Signals using a Hybrid Deep Learning Approach." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.488.
Kumar, Gatta. "Automated Heart Disease Detection from ECG Signals using a Hybrid Deep Learning Approach." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.488.
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References
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 18 2026
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