RESUMO
Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appearance and routine activities, subtle indications of these diseases can manifest in the electrocardiogram (ECG) signals, often overlooked by standard interpretation. Current machine learning models have been ineffective in discerning these minor variations due to the irregular and subtle nature of changes in the ECG patterns. This paper uses a novel deep-learning approach to predict slight variations in ECG signals by fine-tuning the learning rate of a deep convolutional neural network. The strategy involves segmenting ECG signals into separate data sequences, each evaluated for unique centroid points. Utilizing a clustering approach, this technique efficiently recognizes minute yet significant variations in the ECG signal characteristics. This method is estimated using a specific dataset from SRM College Hospital and Research Centre, Kattankulathur, Chennai, India, focusing on patients' ECG signals. The model aims to predict the ordinary and subtle variations in ECG signal patterns, which were subsequently mapped to a pre-trained feature set of cardiovascular diseases. The results suggest that the proposed method outperforms existing state-of-the-art approaches in detecting minor and irregular ECG signal variations. This advancement could significantly enhance the early detection of cardiovascular diseases, offering a promising new tool in predictive medical diagnostics.
