Abstract
In this paper, a method for the detection of premature ventricular contraction (PVC) beat in electrocardiogram (ECG) signal is presented. The method adopts the minimum distance (MD) and the linear discriminant analysis (LDA) in a proposed feature space for classification. The features are extracted by continuous wavelet-transform of the ECG beat signal followed by the Teager-Kiaser Energy (TKE) operator. The significant TKEs are then selected, transformed to [0, 1] range, and used as a feature vector. The prototypes of different classes in the feature space are generated using the fuzzy c-means (FCM) clustering algorithm. To test the proposed method, the MIT-BIH arrhythmia database has been used. The method has provided total accuracy of 100% in classifying normal and PVC beats with MD classifier. The proposed method has also outperformed a counterpart one employing features extracted by the discrete cosine transform (DCT).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
