Comprehensive ECG Signal Analysis for Heart Disease Classification: Integrating Neural Networks with Random Forest

The heart’s function in circulating blood to our organs is vital, making the early detection of heart disease imperative to prevent severe health outcomes. Electrocardio-graphy (ECG) is a primary diagnostic tool for identifying heart conditions. This study leverages the PTB-XL database to explore the integration of Ensemble Learning and Deep Learning techniques for automated ECG signal analysis. We implement a hybrid model that combines Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers to effectively capture the temporal dynamics and complex patterns in ECG signals. To further enhance the robustness and generalizability of our model, we integrate Random Forest (RF) ensemble classifiers. Our comprehensive evaluation of the PTB-XL dataset indicates that this integrated approach successfully distinguishes between heart disease and normal signals, surpassing the performance of existing single-model methods. Additionally, we conduct extensive comparative analysis to ensure the reliability and validity of our findings. The results demonstrate the effectiveness and reliability of our method for ECG signal classification, offering significant potential for clinical application in improving the early detection and diagnosis of heart diseases. This research highlights the promising role of hybrid ensemble learning and deep learning models in medical diagnostics, paving the way for more accurate and automated cardiovascular disease screening.