title: How the choice of samples for building arrhythmia classifiers impact their performances.
abstract: Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact this choice has on their performance (i.e., accuracy/sensitivity/specificity). From our implementation, we also report new accuracies for these methods, establishing a new state-of-the-art method, in terms of results.
authors.affiliation: Department of Computing, Universidade Federal de Ouro Preto, 35400-000 Ouro Preto, MG, Brazil. feduluz@gmail.com
authors.forename: Eduardo
authors.initials: E
authors.lastname: Luz
authors.forename: David
authors.initials: D
authors.lastname: Menotti
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D016477:Artifacts; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity