A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification

In this work, we propose a novel approach to detect cardiac arrhythmias in electrocardiograms (ECG). The proposal focuses on different feature extractors and machine learning methods. The feature extraction techniques evaluated were Fourier, Goertzel, Higher Order Statistics (HOS), and Structural Co-Occurrence Matrix (SCM). As far as the authors know, this is the first time that SCM has been applied to the feature extraction task with ECG signals. Four well-known classifiers, commonly referred to in the literature (Support Vector Machine, Multi-Layer Perceptron, Bayesian, and Optimum-Path Forest) were tested and we compared our results with six classical feature extraction methods. Furthermore, the Association for the Advancement of Medical Instrumentation protocol was adopted and we made use of the MIT-BIH Arrhythmia Database for producing reliable results for clinical analysis. The confidence level to identify heart dysrhythmia in our results was 2% greater than other approaches in the literature. The proposed system is 1.3% more accurate than the best approach reported to date, and is 10(6) times faster. blackln short, it is clinical reliable to use HOS for describing types of arrhythmia, since achieved 94.3% of accuracy. (C) 2019 Elsevier B.V. All rights reserved.