title: Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.
abstract: This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database.
authors.affiliation: University of Technology, Warsaw, Poland. sto@iem.pw.edu.pl
authors.forename: Stanislaw
authors.initials: S
authors.lastname: Osowski
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Siwek
authors.forename: Robert
authors.initials: R
authors.lastname: Siroic
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer