Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients


KUTLU Y., GÜRKAN KUNTALP D.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.105, no.3, pp.257-267, 2012 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 105 Issue: 3
  • Publication Date: 2012
  • Doi Number: 10.1016/j.cmpb.2011.10.002
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.257-267
  • Keywords: Wavelet packet decomposition, Higher order statistics, Classification, Arrhythmia, ECG beat, Heartbeat, k-nearest neighbors, SUPPORT VECTOR MACHINES, VENTRICULAR-FIBRILLATION, BEAT CLASSIFICATION, NEURAL-NETWORKS, RECOGNITION, MORPHOLOGY, TRANSFORM
  • Dokuz Eylül University Affiliated: Yes

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.