A New QRS Detection Method Using Wavelets and Artificial Neural Networks

Title
A New QRS Detection Method Using Wavelets and Artificial Neural Networks
Author(s)
서희돈버닥[ 버닥]
Keywords
TRANSFORMS
Issue Date
201108
Publisher
SPRINGER
Citation
JOURNAL OF MEDICAL SYSTEMS, v.35, no.4, pp.683 - 691
Abstract
We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.
URI
http://hdl.handle.net/YU.REPOSITORY/24789http://dx.doi.org/10.1007/s10916-009-9405-3
ISSN
0148-5598
Appears in Collections:
공과대학 > 전자공학과 > Articles
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