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

• 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
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2011.10.002

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.