Deep Learning Approaches with Hybrid Feature Extraction for ECG Signal Classification

The growing prevalence of cardiovascular diseases necessitates the development of efficient and accurate diagnostic tools. Electrocardiogram (ECG) signal classification has emerged as a promising method for improving the diagnosis and early detection of heart diseases. This study presents a comparative analysis of various deep learning algorithms, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Hybrid CNN-LSTM, for the classification of ECG signals. By leveraging advanced feature extraction techniques, we explore the effectiveness of these models in distinguishing between normal and abnormal ECG patterns. The proposed methodology demonstrates the potential of deep learning in automating ECG signal analysis, thereby enhancing diagnostic accuracy and reducing the reliance on traditional, time-consuming manual interpretations. Additionally, the integration of these techniques offers a cost-effective solution, particularly in resource-constrained settings where access to expert cardiologists may be limited. The results of this study contribute to the ongoing efforts to improve heart disease detection systems using deep learning.

Keywords: ECG Classification, Deep Learning, CNN, LSTM, Hybrid CNN-LSTM, Feature Extraction, Cardiovascular Diseases, Early Diagnosis, Signal Analysis, Automated Diagnosis.