Intelligent Systems to Autonomously Classify Several Arrhythmia Using Information from ECG

A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals

“…On the other hand, many researchers have used numerous techniques to reduce the dimension of feature space and to determine the most relevant features (Yang and Zhidong 2017). To this end, mutual information (MILCA, mRMR, NMIFS) (Valenzuela et al 2013), genetic algorithms (GAs) (Silva Teodoro, Peres, and Lima 2017), multisession feature selection (MSFS) (Komeili et al 2017), and dynamic programming (DP) algorithm (Acir 2005) have been employed. In addition, it is clear that many popular classification methods (Cömert and Kocamaz 2017a), such as fractal analysis (Lai and Chan 1998), chaotic modeling (Owis et al 2002), bispectral coherence analysis (Khadra, Al-Fahoum, and Binajjaj 2005), and radial basis networks (Maglaveras et al 1998) In this study, we obtained several features from morphological and statistical domains to describe ECG signals after the preprocessing stage.…”