Comput Methods Programs Biomed - Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients.

Tópicos

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Resumo

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.

Resumo Limpo

paper describ featur extract method use higher order statist hos wavelet packet decomposit wpd coeffici purpos automat heartbeat recognit method consist three stage first wavelet packag coeffici wpc calcul differ type ecg beat higher order statist wpc deriv final obtain featur set use input classifi base knn algorithm mitbih arrhythmia databas use obtain ecg record use studi heartbeat arrhythmia databas group five main heartbeat class classif accuraci propos system measur averag sensit averag select averag specif result show hos wpc featur high discrimin classif differ arrhythm ecg beat

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