ECG Signal Analysis for Myocardial Disease Prediction by Classification with Feature Extraction Machine Learning Architectures

Abstract

An effective method for early diagnosis of numerous cardiovascular disorders is the electrocardiogram (ECG) signal. This method is particularly useful for detecting arrhythmias, which are irregular heartbeats. This research proposes novel technique in ECG signal analysis for myocardial disease prediction using machine learning architectures-based feature extraction and classification. Here the input is taken as ECG signal for detecting myocardial disease and process the signal for noise removal. The processed signal features are extracted using principal component analysis and classified using fast fourier vector machine. The objective of the work, the algorithms used, and the outcomes are the qualitative and qualitative parameters used to compare and contrast the existing methodologies. The experimental analysis has been carried out in terms of accuracy, precision, recall, F_1 score, SNR and RMSE.