Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
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Date
2012
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ireland Ltd
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
Description
Keywords
Wavelet packet decomposition, Higher order statistics, Classification, Arrhythmia, ECG beat, Heartbeat, k-nearest neighbors
Journal or Series
Computer Methods and Programs in Biomedicine
WoS Q Value
Q1
Scopus Q Value
Q1
Volume
105
Issue
3
