title: Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification.
abstract: The present work proposes the development of an automated medical diagnostic tool that can classify ECG beats. This is considered an important problem as accurate, timely detection of cardiac arrhythmia can help to provide proper medical attention to cure/reduce the ailment. The proposed scheme utilizes a cross-correlation based approach where the cross-spectral density information in frequency domain is used to extract suitable features. A least square support vector machine (LS-SVM) classifier is developed utilizing the features so that the ECG beats are classified into three categories: normal beats, PVC beats and other beats. This three-class classification scheme is developed utilizing a small training dataset and tested with an enormous testing dataset to show the generalization capability of the scheme. The scheme, when employed for 40 files in the MIT/BIH arrhythmia database, could produce high classification accuracy in the range 95.51-96.12% and could outperform several competing algorithms.
authors.affiliation: Heritage Institute of Technology, Electrical Engineering Department, Chowbaga Road, Anandapur, Kolkata, West Bengal 700107, India. saibal_ dutta2001@yahoo.com
authors.forename: Saibal
authors.initials: S
authors.lastname: Dutta
authors.forename: Amitava
authors.initials: A
authors.lastname: Chatterjee
authors.forename: Sugata
authors.initials: S
authors.lastname: Munshi
mesh_terms: D001145:Arrhythmias, Cardiac / Q000145:classification / Q000175:diagnosis*; D001185:Artificial Intelligence; D003661:Decision Support Techniques; D004562:Electrocardiography / Q000145:classification / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity