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Reseach Article

Optimized Noise Canceller for ECG Signals

Published on None 2011 by Suman, Swapna Devi, Malay Dutta
Intelligent Systems and Data Processing
Foundation of Computer Science USA
ICISD - Number 1
None 2011
Authors: Suman, Swapna Devi, Malay Dutta

Suman, Swapna Devi, Malay Dutta . Optimized Noise Canceller for ECG Signals. Intelligent Systems and Data Processing. ICISD, 1 (None 2011), 10-17.

author = { Suman, Swapna Devi, Malay Dutta },
title = { Optimized Noise Canceller for ECG Signals },
journal = { Intelligent Systems and Data Processing },
issue_date = { None 2011 },
volume = { ICISD },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 10-17 },
numpages = 8,
url = { /specialissues/icisd/number1/2312-22/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Special Issue Article
%1 Intelligent Systems and Data Processing
%A Suman
%A Swapna Devi
%A Malay Dutta
%T Optimized Noise Canceller for ECG Signals
%J Intelligent Systems and Data Processing
%@ 0975-8887
%N 1
%P 10-17
%D 2011
%I International Journal of Computer Applications

During the acquisition of Electrocardiogram Signals (ECG), various interferences distort the signal. Adaptive filters have been widely used as noise cancellers. Traditional optimization techniques have been very popular because of their advantages. Least Mean Square (LMS) is a traditional optimization technique which is gradient based. This method converges very quickly to an optimal solution and is easy to understand. But this technique does not provide solutions for non-differentiable and discontinuous problems. Bio-inspired optimization algorithms such as genetic algorithm (GA) and Memetic algorithm (MA) can optimize complex and hard problems. In this paper, the adaptive noise canceller has been optimized with Modified Memetic Algorithm (MMA) to remove power line interference in the ECG signals. The performance of these algorithms has been analyzed on the basis of parameters viz., improvement in signal to noise ratio, normalized correlation coefficient (NCC) and root mean square error (RMSE). The results show that (MMA) outperforms both LMS and GA algorithms. Simulation results of GA and MA on benchmark functions viz. Greiwank and Rastrigin show that MMA is more effective for the optimization process.

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Index Terms

Computer Science
Information Sciences


Empirical Mode Decomposition Genetic algorithm Least Mean Square Algorithm Memetic Algorithm Benchmark Functions