Application of fuzzy neural network in atherosclerosis

Author

Zhou, Runjing ; Li, Lin

Author_Institution

Coll. of Electron. Inf. Eng., Inner Mongolia Univ., Hohhot, China

Volume

2

fYear

2011

fDate

15-17 Oct. 2011

Firstpage

635

Lastpage

639

Abstract

This paper describes the use of Wavelet Transform interferences rejection in ECG, pulse signals to eliminate baseline drift, power-line interference and muscle electricity, and then based on wavelet singularity theory in electrocardiograph signal to R-wave, QRS complex width T-wave amplitude is detection and orientation, extract characteristic values. Also extract time domain and frequency values In pulse signals. Through fuzzy neural networks is classify the signal to achieve non-destructive diagnosis of arteriosclerosis.

Keywords

diseases; electrocardiography; fuzzy logic; medical signal processing; neural nets; signal classification; signal denoising; wavelet transforms; ECG pulse signals; QRS complex; R-wave; T-wave amplitude; arteriosclerosis nondestructive diagnosis; atherosclerosis; baseline drift elimination; electrocardiograph signal; fuzzy neural network; muscle electricity elimination; power line interference elimination; signal classification; wavelet singularity theory; wavelet transform interference rejection; Atherosclerosis; Electrocardiography; Feature extraction; Fuzzy neural networks; Interference; Wavelet analysis; Wavelet transforms; Neural Network; Wavelet Transform; atherosclerosis; electrocardiograph signal; pulse signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-9351-7

Type

conf

DOI

10.1109/BMEI.2011.6098484

Filename

6098484