title: Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods.
abstract: Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap to some extent. In the first and second method, Reconstructed phase space (RPS) is modeled by the Gaussian mixture model (GMM) and bins, respectively, and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before, for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% classification accuracy.
authors.affiliation: Biomedical Engineering Faculty, AmirKabir University of Technology, Tehran Polytechnic, 424 Hafez Ave., Tehran, Iran. i_nejadgholi@aut.ac.ir
authors.forename: Isar
authors.initials: I
authors.lastname: Nejadgholi
authors.forename: Mohammad Hasan
authors.initials: MH
authors.lastname: Moradi
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Abdolali
mesh_terms: D001145:Arrhythmias, Cardiac; D001499:Bayes Theorem; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D016011:Normal Distribution; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted