Advanced ECG Signal Classification Using Deep Learning Networks for Early Disease Diagnosis in Healthcare
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P102Keywords:
ECG Signal, Smart Health System, Disease Prediction, Electrocardiograms, Signal Image Processing, ECG DatasetAbstract
Healthcare is producing vast amounts of data, providing new opportunities for early disease detection and improved patient care. Electrocardiograms (ECGs) are widely used to identify cardiac abnormalities; however, manual interpretation is often time-consuming and prone to inaccuracy. This study proposes an advanced deep learning framework for automated ECG signal classification, enabling precise and rapid identification of cardiac disorders. The approach involves careful data preprocessing noise elimination, augmentation, normalization, and feature extraction followed by training a Recurrent Neural Network (RNN) to capture the time-dependent dynamics of sequential ECG data. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC metrics. The RNN model is compared against baseline models such as Convolutional Neural Networks (CNN), Random Forest (RF), and ResNet50. Results show that while CNN achieved 49% accuracy, RF reached 83.7%, and ResNet50 achieved 94.02%, the proposed RNN attained superior performance with 99.5% accuracy, 99.4% precision, 99.2% recall, and a 99.2% F1-score. These results highlight the RNN’s effectiveness in detecting subtle cardiac irregularities. The proposed framework significantly reduces human error, accelerates clinical decision-making, and enhances patient outcomes, demonstrating strong potential for real-world healthcare applications and early disease diagnosis.
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