Performance Comparison of Wavelet and Multiwavelet Denoising Methods for an Electrocardiogram Signal
Author(s) -
Balambigai Subramanian,
Asokan Ramasamy,
Kamalakannan Rangasamy
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/241540
Subject(s) - noise reduction , wavelet , pattern recognition (psychology) , noise (video) , computer science , signal (programming language) , artificial intelligence , wavelet transform , speech recognition , image (mathematics) , programming language
The increase in the occurrence of cardiovascular diseases in the world has made electrocardiogram an important tool to diagnose the various arrhythmias of the heart. But the recorded electrocardiogram often contains artefacts like power line noise, baseline noise, and muscle artefacts. Hence denoising of electrocardiogram signals is very important for accurate diagnosis of heart diseases. The properties of wavelets and multiwavelets have better denoising capability compared to conventional filtering techniques. The electrocardiogram signals have been taken from the MIT-BIH arrhythmia database. The simulation results prove that there is a 29.7% increase in the performance of multiwavelets over the performance of wavelets in terms of signal to noise ratio (SNR)
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