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http://dx.doi.org/10.13067/JKIECS.2012.7.4.727

Classification of ECG arrhythmia using Discrete Cosine Transform, Discrete Wavelet Transform and Neural Network  

Yoon, Seok-Joo (전남대학교 전기.전자통신.컴퓨터공학부)
Kim, Gwang-Jun (전남대학교 전기.전자통신.컴퓨터공학부)
Jang, Chang-Soo (전남대학교 전기.전자통신.컴퓨터공학부)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.7, no.4, 2012 , pp. 727-732 More about this Journal
Abstract
This paper presents an approach to classify normal and arrhythmia from the MIT-BIH Arrhythmia Database using Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) and neural network. In the first step, Discrete Cosine Transform is used to obtain the representative 15 coefficients for input features of neural network. In the second step, Discrete Wavelet Transform are used to extract maximum value, minimum value, mean value, variance, and standard deviation of detail coefficients. Neural network classifies normal and arrhythmia beats using 55 numbers of input features, and then the accuracy rate is 98.8%.
Keywords
Electrocardiogram; Arrhythmia; Discrete Cosine Transform; Discrete Wavelet Transform;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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