Prediction of Confidence Score of Myocardial Infarction using Multiscale Energy and Eigenspace Features

In this research paper we propose a novel technique is to predict the Confidence Score of Myocardial Infarction from multilead electrocardiogram (ECG) signals. Classifying the confidence score (CS) of myocardial infarction (MI) severity provides a quantitative measure of the severity level, enabling accurate assessment. MI v/s non MI is used in automated diagnosis but CS is not used. If any patients having MI then level of disease can be captured by CS which can help cardiologist for better treatment planning. ECG morphological alterations associated with the progression of MI show pathological traits such T-wave inversions, changes in ST elevation/depression, pathological Qwaves, hypercute T-waves. Multiscale wavelet energies and eigen values of multiscale covariance matrices are used as diagnostic features for prediction of confidence score of MI. As classifiers, K-nearest neighbor (KNN), Decision Tree, Random Forest, AdaBoost and Gradient Boosting are used. The PTB-XL database used for evaluation, comprises different types of MI, healthy control (HC) subjects, and five distinct classes of confidence scores (15, 35, 50, 80, 100). The result for classification of HC and MI with confidence score evaluated accuracy & F1 score of 96.7%, 98.5% respectively on PTB-XL database using Random forest which is better than existing state of art. The overall accuracy performance of CS of MI for CS =15, CS =35, CS =80, CS =100 are are 98.2%, 63%, 63.7% and 90.5% respectively using Gradient boosting classifier. The usage of various classifiers demonstrates their effectiveness in predicting different classes of confidence scores, providing valuable insights for further analysis.