ECG Signal Classification using PCA, DWT and Neural Network Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.5 · Issue 9

Published

April 2017

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Open Access

Licence

CC BY-NC-SA 4.0

Ankish Dangi Dr. G. D. Gidwani

This paper offers ECG signal classification system using Principal Component Analysis (PCA) technique to reduce the dimensionality of test signal. Discrete Wavelet Transform (DWT) is used for feature extraction. Power Spectral Density (PSD) is another feature for the spectrum of ECG. This process helps in enhancing the classification accuracy. Classification is done using Neural Network classifier. In this paper, the signal processing and neural network toolbox are used in MATLAB environment. The processed signal source came from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database which was developed for research in cardiac electrophysiology.

Ankish Dangi, Dr. G. D. Gidwani (2017). ECG Signal Classification using PCA, DWT and Neural Network Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.5, Issue 9. ISSN: 2319-4863.

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