ECG Classification Framework for Cardiac Disease Prediction Using Nonlinear Vector Decomposed Neural Network

Abstract

Early detection of heart disease may prevent myocardial infarction. Electrocardiogram (ECG) is the most widely used signal in clinical practice for the diagnosis of cardiovascular diseases such as arrhythmias and myocardial infarction. Human interpretation is time-consuming, and long-term ECG records are difficult to detect in small differences.Therefore, automated recognition of myocardial infarction using a Computer-Aided Diagnosis (CAD) system is the research interest, which can be used effectively to reduce mortality among cardiovascular disease patients. The most important step in the analysis of complex R-peak/QRS signals using an automated process of ECG signal. To automate the cardiovascular disease detection process, an adequate mechanism is required to characterize ECG signals, which are unknown features according to the similarities between ECG signals. If the classification can find similarities accurately and the probability of arrhythmia detection increases, the algorithm can become an effective method in the laboratory. In this research work, a new classification strategy is proposed to all the more precisely order ECG signals dependent on a powerful model of ECG signals. In this proposed method, a Nonlinear Vector Decomposed Neural Network (NVDN) is developed, and its simulation results show that this classifier can isolate the ECGs with high productivity. This proposed technique expands the exactness of the ECG classification concerning increasingly exact arrhythmia discovery.