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

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 ea...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2012-03, Vol.105 (3), p.257-267
Main Authors: Kutlu, Yakup, Kuntalp, Damla
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia
Arrhythmias, Cardiac - classification
Arrhythmias, Cardiac - diagnosis
Biological and medical sciences
Classification
Databases, Factual
ECG beat
Electrocardiography - methods
Heart Rate - physiology
Heartbeat
Higher order statistics
Humans
Internal Medicine
k-nearest neighbors
Medical sciences
Other
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Signal Processing, Computer-Assisted
Technology. Biomaterials. Equipments. Material. Instrumentation
Wavelet packet decomposition
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Summary: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.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2011.10.002