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.

1. Tang Jing-tian, Zou Qing, Tang Yan, Liu Bin, Zhang Xiao-kai, “Hilbert-Huang Transform for ECG De-noising” Bioinformatics and Biomedical Engineering, ICBBE, Year: 2007, pp. 664 – 667.
2. Zhao Zhidong, Pan Min, “ECG Denoising by Sparse Wavelet Shrinkage” Bioinformatic and Biomedical Engineering, ICBBE, The 1st International Conference on 6-8 July2007, pp.786–789.
3. Yunfeng Wu, Rangayyan, R.M., “An Algorithm for Evaluating the Performance of Adaptive Filters for the Removal of Artifacts in ECG Signals” Electrical and Computer Engineering, CCECE, Canadian Conference on, Year: 2007, pp. 864–867.
4. Omid Sayadi, Mohammad Bagher Shamsollahi, “ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure”, Biomedical Engineering, IEEE Transactions, Volume 55, Issue: 9, 2008, pp. 2240-2248.
5. Delechelle, E., Lemoine, J., Oumar Niang, “Empirical Mode Decomposition: An Analytical Approach for Sifting Process”, Signal Processing Letters, IEEE Volume 12, Issue: 11, Year: 2005, pp. 764 – 767.
6. Nianqiang Li, Ping Li, “An Improved Algorithm Based on EMD-Wavelet for ECG Signal De-noising”, International Joint Conference on Computational Sciences and Optimization, IEEE Computer Society, Year: 2009, pp. 825-827.
7. Nimunkar, A.J., Tompkins, W.J., “EMD-based 60-Hz Noise Filtering of the ECG” Engineering in Medicine and Biology Society EMBS, 29th Annual International Conference of the IEEE, Year: 22-26 Aug. 2007, pp.1904 - 1907.
8. Boudraa, A.-O., Cexus, J.-C., “EMD-Based Signal Filtering”, Instrumentation and Measurement, IEEE Transactions on Volume: 56, Issue: 6 Year: 2007, pp. 2196 – 2202.
9. Widrow, B., Glover, J.R., Jr., McCool, J.M., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.R., Eugene Dong, Jr., Goodlin, R.C., “Adaptive Noise Cancelling: Principles and Applications”. Proceedings of the IEEE Volume 63, Issue: 12, Year: 1975, pp. 1692 – 1716.
10. Zhao Zhidong, Ma Chan, “A Novel Cancellation Method of Powerline Interference in ECG Signal Based on EMD and Adaptive filter”, Communication Technology, ICCT, 11th IEEE International Conference on 10-12 Nov. 2008, pp.517 – 520.
11. Soroor Behbanani, “Investigation of Adaptive Filtering for Noise Cancellation in ECG Signals” Second International Multisymposium on Computer and Computational Sciences, IEEE Computer Society, Year: 2007, pp.144 – 149.
12. M. Srinivas and Lalit M. Patnaik, “Genetic Algorithms: A Survey”, Computer Volume 27, Issue: 6, Year: 1994, pp. 17 – 26.
13. Nguyen, Q.H., Ong, Y.S., Krasnogor, N., “A Study on the Design Issues of Memetic Algorithm”, Evolutionary Computation, CEC, Year: 2007, pp. 2390 – 2397.
14. Digalakis, J.G.; Margaritis, K.G.; "An experimental study of benchmarking functions for genetic algorithms," IEEE International Conference on Systems, Man, and Cybernetics, 2000, vol.5, no., pp.3810-3815.
15. Pant, M.; Thangaraj, R.; Abraham, A., "Particle Swarm Based Meta-Heuristics for Function Optimization and Engineering Applications," Computer Information Systems and Industrial Management Applications, 2008. CISIM '08. 7th, vol., no., 26-28 June 2008, pp.84-90.
16. Ratnaweera, A.; Halgamuge, S.K.; Watson, H.C.;, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Transactions on Evolutionary Computation, vol.8, no.3, June 2004, pp. 240- 255.

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