Baseline wandering can mask some important features of the Electrocardiogram (ECG) signal hence it is desirable to remove this noise for proper analysis and display of the ECG signal. This paper presents the implementation and evaluation of different methods to remove this noise. The parameters i.e. Power Spectral density (PSD), average Power & Signal to noise ratio (SNR) are calculated of signals to compare the performance of different filtering methods. IIR zero phase filtering has been proved efficient method for the removal of Baseline wander from ECG signal. The results have been concluded using Matlab software and MIT-BIH arrhythmia database.

1. Gulrajani, Ramesh M., “Bioelectricty and Biomagnetism’’, John Wiley and Sons Publishing Company, 1998.
2. Wikipedia.
3. Yunfeng Wu, Rangaraj M. Rangayyan , Yachao Zhou, Sin-Chun Ng, ‘Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system’, Medical Engineering & Physics 31 (2009) 17–26.
4. Behzad Mozaffary Mohammad A. Tinati, ‘ECG Baseline Wander Elimination using Wavelet Packets’, World Academy of Science, Engineering and Technology 3 2005,pp 14-16.
5. Zahoor-uddin, ‘Baseline Wandering Removal from Human, Electrocardiogram Signal using Projection Pursuit Gradient Ascent Algorithm’, International Journal of Electrical & Computer Sciences IJECS/IJENS Vol: 9 No: 9, pp 11-13
6. Mahesh S. Chavan, R.A.Agarwala, M.D.Uplane, Comparative Study of Chebyshev I and Chebyshev II Filter used For Noise Reduction in ECG Signal, International Journal Of Circuits, Systems And Signal Processing Issue 1, Volume 2, 2008,pp1-17
7. Mahesh S. Chavan, R.A.Aggarwala, M.D.Uplane, ‘Suppression Of Baseline Wander And Power Line Interference in ECG Using Digital IIR Filter’, International Journal of Circuits, Systems And Signal Processing, Issue 2, Volume 2, 2008,pp-356-65.
8. Fayyaz A. Afsar, M. S. Riaz and M. Arif, ‘A Comparison of Baseline Removal Algorithms for Electrocardiogram (ECG) based Automated Diagnosis of Coronory Heart Disease’ 3rd International Conference on Bioinformatics & Biomedical Engineering (iCBBE-2009), Beijing, PR of China, June 11-13, 2009.
9. V. S. Chouhan and S. S. Mehta, "Total Removal of Baseline Drift from ECG Signal, Computing: Theory and Applications-ICCTA '07, 0-7695-2770-1/07
10. Matlab help, MATLAB MATHWORKS. http://www mathworks.com
11. Leif Sornmo, & Pablo Laguna, ‘Electrocardiogram (ECG) Signal Processing’. Wiley Encyclopedia of Biomedical Engineering, 2006 John Wiley & Sons, Inc., pp-1-16
12. Matti Aksela, ‘ECG Filtering’ T-61.181 Biomedical Signal Processing Presentation 11.11.2004.
13. Technikum Wien, ‘Microprocessor based Design for Biomedical Applications‘, MBE 3 – MDBA VII: Digital Signal Processing Basics & Applications. embsys.technikum-wien.at/staff/veigl/MDBA/.../I-Introduction.ppt
14. ‘Filters II – Filter Design with MATLAB’, Experiment 3, pp 4-5.,www.cas.mcmaster.ca/~lawford/.
15. Mahesh S. Chavan, R.A.Agarwala, M.D.Uplane, ‘Use of Kaiser Window For ECG processing’, Proceedings of the 5th WSEAS Int. Conf. on Signal Processing, Robotics and Automation, Madrid, Spain, February 15-17, 2006 (pp285-289).
16. Lisette P. Harting et al., ‘On Baseline drift Suppressing In ECG-Recordings’, Proceedings of the 2004 IEEE Benelux Signal Processing Symposium, pp133-136
17. Ratnakar Madan et al, ‘Signal Filtering Using Discrete Wavelet Transform’, International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009,pp 96-98.
18. C. R. Meyer and H. N. Keiser., ‘Electrocardiogram baseline estimation and removal using cubic splines and space-state computation techniques’, Computers and biological research, volume 10, issue 5, 1977, pages 459-470.

Baseline wander Filtering wavelet Polynomial fitting PSD