Adaptive wavelet representation and classification of ECG signals

Author

Kanapathipillai, Murale ; Jouny, Ismail ; Hamilton, Patrick

Author_Institution

Dept. of Electr. Eng., Lafayette Coll., Easton, PA, USA

fYear

1994

fDate

1994

Firstpage

1310

Abstract

ECG signals are adaptively approximated as a weighted linear combination of translated and dilated mother wavelets. An ECG frame is thus represented by a limited number of adaptively estimated parameters indicating translation, scaling, and weights. Also, a neural network classifier that utilizes adaptive wavelet based features is used to discriminate between normal and abnormal beats. The ECG signals used in the experimental phase of this study are extracted from the “MIT/BIH” arrhythmia database

Keywords

wavelet transforms; ECG frame; ECG signal classification; MIT/BIH arrhythmia database; abnormal beats; adaptive wavelet based features; adaptive wavelet representation; adaptively estimated parameters; dilated mother wavelets; neural network classifier; normal beats; scaling; translated mother wavelets; translation; weighted linear combination; weights; Data compression; Data mining; Educational institutions; Electrocardiography; Least squares approximation; Linear approximation; Neural networks; Parameter estimation; Spatial databases; Speech;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-2050-6

Type

conf

DOI

10.1109/IEMBS.1994.415447

Filename

415447