The week's pick
Random Articles
Reseach Article
A Study of Sensitivity of a Least Mean Square Filter on its Tap Weight Vector Length and Step Size in Adaptively Cancelling Noise in an Electrocardiogram Signal
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 152 - Number 8 |
| Year of Publication: 2016 |
| Authors: Sushmita Haldar |
10.5120/ijca2016911899
|
Sushmita Haldar . A Study of Sensitivity of a Least Mean Square Filter on its Tap Weight Vector Length and Step Size in Adaptively Cancelling Noise in an Electrocardiogram Signal. International Journal of Computer Applications. 152, 8 ( Oct 2016), 1-3. DOI=10.5120/ijca2016911899
Abstract
Electrocardiogram (ECG) is a procedure that records the activity of the heart and presents it as an electrical signal. It is an immensely important diagnostic tool since any deviation from the characteristic shape of an ECG may point to a cardiac anomaly, making it important for instrument and processing to be very accurate. Since the nature of noise that could corrupt the ECG is non-stationary, Adaptive Noise Cancellation (ANC) filters are required. In this paper, the Adaptive Noise Cancellation algorithm Least Mean Squares (LMS) has been implemented to reduce the noise in a corrupted ECG signal. The parameter sensitivity of the filter on its result had been studied, how the values of tap weight vector length and step size of the filter influence the quality of the resultant signal of the filter. This has been done through simulations in MATLAB, the noise have been simulated in MATLAB and the ECG signals have been collected from the ECG-ID database at PhysioNet. For various values of tap weight vector length and step size the performance of the LMS filter is analyzed in terms of PRD and MSE and the observations are tabulated. At the end of this study parameter values are suggested that render the most optimum results. GENERAL TERMS Adaptive Noise Cancellation, Parameter Sensitivity.
References
- Simon Haykin: “Adaptive Filter Theory”, Third Edition, Prentice Hall, Inc., Upper Saddle River, NJ, 1996.
- John G. Proakis, Dimitris G. Manolakis: “Digital Signal Processing”, Fourth Edition, Pearson, pp. 905-907.
- T. Pitchaiah, P. V. Sridevi and S. K. Rao, "Adaptive noise cancellation using LMS algorithm in Monte Carlo simulation," Electronics and Communication Systems (ICECS), 2015 2nd International Conference on, Coimbatore, 2015, pp. 368-372.
- G. Makwana and L. Gupta, "De-noising of Electrocardiogram (ECG) with Adaptive Filter Using MATLAB," Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on, Gwalior, 2015, pp. 511-514.
- A. C. Mugdha, F. S. Rawnaque and M. U. Ahmed, "A study of recursive least squares (RLS) adaptive filter algorithm in noise removal from ECG signals,"Informatics, Electronics & Vision (ICIEV), 2015 International Conference on, Fukuoka, 2015, pp. 1-6.
- U. Biswas, A. Das, S. Debnath and I. Oishee, "ECG signal denoising by using least-mean-square and normalised-least-mean-square algorithm based adaptive filter,"Informatics, Electronics & Vision (ICIEV), 2014 International Conference on, Dhaka, 2014, pp. 1-6.
- A. Mousa, M. Qados and S. Bader, "Adaptive noise cancellation algorithms sensitivity to parameters," Multimedia Computing and Systems (ICMCS), 2011 International Conference on, Ouarzazate, 2011, pp. 1-5.
- R. Nagal, P. Kumar and P. Bansal, "Performance analysis of least mean square algorithm for different step size parameters with different filter order and iterations,"Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015 International Conference on, Noida, 2015, pp. 326-331.
Index Terms
Keywords