Detection of VPC using wavelet transform and fuzzy neural network

Author

Wu, Ying-Hsuan ; Shyu, Liang-Yu

Author_Institution

Dept. of Biomed. Eng., Chung Yuan Christian Univ., Chung Li, Taiwan

Volume

2

fYear

1999

fDate

36434

Abstract

A combination of fuzzy neural network (FNN) and wavelet transform (WT) is proposed to identify the ventricular premature contraction (VPC) beats from the Holter ECG. First, the Holter ECG is transformed into different scales using a quadratic spline wavelet. Three features, including the widths of the QRS complex at scales 2³ and 2 ⁴ and the area of QRS at scale 2⁵, are extracted and input into a four-layer FNN. The Gaussian function is adapted as the membership function in the FNN. In addition, during the training period, the backpropagation algorithm is used to train this FNN. The preliminary results indicate that the proposed method can achieve high accuracy in the detection of VPC

Keywords

backpropagation; cardiovascular system; electrocardiography; fuzzy neural nets; medical signal detection; medical signal processing; wavelet transforms; Gaussian function; Holter ECG; QRS complex; VPC detection; backpropagation algorithm; four-layer FNN; fuzzy neural network; membership function; quadratic spline wavelet; training period; ventricular premature contraction beats; wavelet transform; Backpropagation algorithms; Biomedical engineering; Electrocardiography; Fuzzy neural networks; Fuzzy reasoning; Heart beat; Heart rate variability; Morphology; Spline; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.804366

Filename

804366