Comparison of Wavelet and Filtering Techniques for Denoising ECG Signal
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Date
2014
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Faculty of Engineering, Bayero University Kano, Nigeria
Abstract
A proper processing of biomedical
signals enhances their physiological and clinical
information because they carry vital information
about the behaviour of the living systems under
study. With the analysis of the
Electrocardiogram (ECG) signal it may be
possible to predict heart problems and play an
important role in diagnosis process or monitor
patient recovery after a heart intervention. The
quality of this signal is degraded mainly by
many sources of noise such as power line
interference (PLI), baseline drift, muscle
contraction noise etc. Present work deals with
the design of filter banks based on the discrete
wavelet transform (DWT) as well as design of
low pass Chebyshev Type I and Butterworth
filters using FDA tool in MATLAB environment.
De-noised ECG signal is compared with original
signal using Mean Square Error (MSE). Results
show that denoising schemes involving wavelet
domains are able to reduce noise from ECG
signals more accurately and consistently with
mse of 0.0012 in comparison to noise reduction
algorithms in filtering technique which has mse
of 0.0799 and 0.1814 for Chebyshev and
Butterworth filters respectively.
Description
Keywords
ECG, DWT, Butterworth, Chebyshev, MSE