ECG arrhythmia classification based on optimum-path forest - Luz, Eduardo José Da S.; Nunes, Thiago M.; De Albuquerque, Victor Hugo C.; Papa, João Paulo [UNESP]; Menotti, David (Elsevier, 2013)
An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.
Keywords: Bayesian; ECG classification; Feature extraction; Multilayer artificial neural network; Optimum-path forest; Support vector machine; Arrhythmia classification; Bayesian classifier; Cardiac arrhythmia; Cardiac rhythms; Classification time; Computational costs; Ecg classifications; Electrocardiogram signal; Evaluation protocol; Feature extraction and classification; Graph-based; Heart disease diagnosis; Heartbeat signals; Medical instrumentation; Multilayer artificial neural networks; Optimum-path forests; Pattern recognition techniques; Robust performance; Signal classification; SVM classifiers; Training and testing; Diseases; Expert systems; Forestry; Multilayers; Neural networks; Support vector machines; Electrocardiography; Expert Systems; Neural Networks