Detection of cardiac pathologies in electrocardiogram signals using empirical mode decomposition and neural networks

The purpose of this work is to develop an Artificial Intelligence-based system capable of classifying individuals as either healthy or having a cardiac pathology (Myocardial Ischemia, Cardiomyopathy, Bundle Branch Block, Dysrhythmia, or Ventricular Hypertrophy) based on the processing of Electrocardiogram (ECG) signals. This system aims to serve as a non-invasive and efficient diagnostic tool. The ECG signals used in this study were obtained from a publicly available database. Signal processing is performed using Empirical Mode Decomposition (EMD). From the decomposition, statistical features are extracted from the modes obtained and structured into a feature vector that represents the signal. Classification is achieved using an optimized Neural Network (NN) model, which uses 2580 ECG signals to distinguish between healthy individuals and those with cardiac disease, considering cross-validation. After implementing the system, the mean accuracy achieved was 96.03% for training, 92.06% for validation, and 92.20% for testing. The proposed system successfully demonstrated high classification accuracy for cardiac pathologies while being computationally efficient, making it a valuable tool for preliminary diagnosis and further analysis of ECG signal.