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Performance Study of Various Adaptive filter algorithms for Noise Cancellation in Respiratory Signals
A.Bhavani Sankar, D.Kumar, K.Seethalakshmi
Pages - 267 - 278     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
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KEYWORDS
Adaptive filter , Motion artifact, Power line interference, Least Mean Square (LMS), Normalized LMS (NLMS), Block LMS (BLMS)
ABSTRACT
Removal of noises from respiratory signal is a classical problem. In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the respiratory and other biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. This paper focuses on (i) Model Respiratory signal with second order Auto Regressive process. Then randomly generated noises have been mixed with respiratory signal and nullify these noises using various adaptive filter algorithms (ii) to remove motion artifacts and 50Hz Power line interference from sinusoidal 0.18Hz respiratory signal using various adaptive filter algorithms. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error.
CITED BY (25)  
1 Nasrolahzadeh, M., Mohammadpoori, Z., & Haddadnia, J. (2016). Analysis of mean square error surface and its corresponding contour plots of spontaneous speech signals in Alzheimer's disease with adaptive wiener filter. Computers in Human Behavior, 61, 364-371.
2 Sultana, N., Kamatham, Y., & Kinnara, B. (2015, August). Performance analysis of adaptive filtering algorithms for denoising of ECG signals. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on (pp. 297-302). IEEE.
3 Sankar, A. B., Madhusudhanan, N., & Selvi, J. A. V. Removal of Noise through ANFIS filtering. International Journal of Applied Engineering Research, 10(51), 2015.
4 Ahammed, K., Ershadullah, M., Heru, M. R. I., Islam, S., & Sazzad, Z. P.Design and implementation of digital filter bank to reduce noise and reconstruct the input signals.
5 Biswas, U., Hasan, K. R., Sana, B., & Maniruzzaman, M. (2015, May). Denoising ECG signal using different wavelet families and comparison with other techniques. In Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference on (pp. 1-6). IEEE.
6 Biswas, U., Das, A., Debnath, S., & Oishee, I. (2014, May). ECG signal denoising by using least-mean-square and normalised-least-mean-square algorithm based adaptive filter. In Informatics, Electronics & Vision (ICIEV), 2014 International Conference on (pp. 1-6). IEEE.
7 Biswas, U., & Maniruzzaman, M. (2014, April). Removing power line interference from ECG signal using adaptive filter and notch filter. In Electrical Engineering and Information & Communication Technology (ICEEICT), 2014 International Conference on (pp. 1-4). IEEE.
8 Kiran, G. H. (2014). Noise Cancellation of ECG Signal Using Adaptive Technique. IJRCCT, 3(4), 556-562.
9 Malathi, A., & Karthikeyan, N. (2014, May). Performance analysis of acoustic echo cancellation using Adaptive Neruo Fuzzy Inference System. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1132-1136). IEEE.
10 Dash, I., & Biswal, K. (2014). Noise cancellation of ECG signal using adaptive and Backpropagation neural network algorithms. The international journal of emerging technology and advanced engineering, 4(5).
11 Wang, H. B., Yen, C. W., Liang, J. T., Wang, Q., Liu, G. Z., & Song, R. (2014). A Robust Electrode Configuration for Bioimpedance Measurement of Respiration. Journal of healthcare engineering, 5(3), 313-328.
12 Sharma, Y. Minimization of Interferences in ECG.
13 Satyanarayana, K., Sarma, A. D., Praveen, P. N., Malini, M., & Reddy, D. K. (2013). Mitigation of Cellular Phone Interference in ECG During Emergency Patient Transportation. Cardiovascular Engineering and Technology, 4(4), 544-552.
14 Vega, L. R., & Rey, H. (2013). Stochastic Gradient Adaptive Algorithms. In A Rapid Introduction to Adaptive Filtering (pp. 33-88). Springer Berlin Heidelberg.
15 Palaniappan, R., Sundaraj, K., Ahamed, N. U., Arjunan, A., & Sundaraj, S. (2013). Computer-based respiratory sound analysis: a systematic review. IETE Technical Review, 30(3), 248-256.
16 Wang, J., Tang, L., & Bronlund, J. E. (2013). Surface EMG signal amplification and filtering. International Journal of Computer Applications, 82(1), 15-22.
17 Jung Eui-chul, gimsangyun, gimon, and jogiryang. (2012). Optimal directional synthesis of linear array source. The Journal of Korea Information and Communications Society, 37 (4), 250-259.
18 Thalkar, S., & Upasani, D. Review Paper………… Various Techniques for Removal of Power Line Interference From ECG Signal.
19 Shalini, M. D., & Sreenivasulu, M. P. Denoising Bio-Signals Using Various Adaptive Filters.
20 Sankar, B. A., Kumar, D., & Seethalakshmi, K. (2012). A New Self-Adaptive Neuro Fuzzy Inference System for the Removal of Non-Linear Artifacts from the Respiratory Signal. Journal of Computer Science, 8(5), 621.
21 Dastgheib-Beheshti, B. (2012). Development of a Full Experimental Framework for the Technical Reference Model of Wireless Sensor Networks (Doctoral dissertation, University of Massachusetts Dartmouth).
22 Panda, R. (2012). Removal of Artifacts from Electrocardiogram (Doctoral dissertation, National Institute of Technology Rourkela).
23 Perrin, G. (2012). Multipoint optimization of a loudspeakeer impulse response.
24 Jeong, E. C., Kim, S. Y., Kim, O., & Cho, K. R. (2012). Optimal Directivity Synthesis of Linear array Sources. The Journal of Korean Institute of Communications and Information Sciences, 37(4A), 250-259.
25 Jung Eui-chul, gimsangyun, gimon, & jogiryang. (2012). Optimal orientation of the composite linear array source. Journal of Korea Information and Communications Society, 37 (4), 250-259.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Socol@r  
6 Scribd 
7 SlideShare 
8 PDFCAST 
9 PdfSR 
Tutorial on “Labview for ECG Signal Processing” developed by NI Developer Zone, National Instruments, April 2010, [online] Available at : http://zone.ni.com/dzhp/app/main
Walter Karlen, Claudio Mattiussi, and Dario Floreano, “Sleep and Wake Classification With ECG and Respiratory Effort Signals”, IEEE Transactions on Biomedical Circuits and Systems, 3( 2), April 2009.
. Jean-Marc Valin and Iain B. Collings, “ Interference-Normalized Least Mean Square Algorithm”, IEEE Signal Processing Letters, 14(12), December 2007.
. S. A. Jimaa, A.Simiri, R. M. Shubair, and T. Shimamura, “Collings, “ Interference-Normalized Least Mean Square Algorithm”, IEEE Signal Processing Letters, 14(12), December 2007.
Ahmed I. Sulyman, Azzedine Zerguine, “Convergence and Steady-State Analysis of a Variable Step-Size Normalized LMS Algorithm”, IEEE 2003.
Allan Kardec Barros and Noboru Ohnishi, “MSE Behavior of Biomedical Event-Related Filters” IEEE Transactions on Biomedical Engineering, 44( 9), September 1997.
Bernard Widrow, John R. Glover, John M. Mccool, “Adaptive Noise Cancelling: Principles and Applications”, Proceedings of the IEEE, 63(12), December 1975.
Convergence Evaluation of Variable Step-Size NLMS Algorithm in Adaptive Channel Equalization” IEEE 2009.
Desmond B. Keenan, Paul Grossman, “Adaptive Filtering of Heart Rate Signals for an Improved Measure of Cardiac Autonomic Control”. International Journal of Signal Processing- 2006.
Hideki Takekawa,Tetsuya Shimamura and Shihab Jimaa, “An Efficient and Effective Variable Step Size NLMS Algorithm” IEEE 2008.
Hong Wanl, RongshenFul, Li Shi,“The Elimination of 50 Hz Power Line Interference from ECG Using a Variable Step Size LMS Adaptive Filtering Algorithm” Life Science Journal, 3 (4), 2006.
J.D Bronzino and T.Ning, “Automatic classification of Respiratory signals”, IEEE Proceedings/EMBS 11 th International Conference, pp.669-670, Nov1989, Seattle,WA.
Mohammad Zia Ur Rahman, Rafi Ahamed Shaik and D V Rama Koti Reddy, “Noise Cancellation in ECG Signals using computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetry”, Signal Processing: An International Journal 3(5), November 2009.
Mohammad Zia Ur Rahman, Rafi Ahamed Shaik and D V Rama Koti Reddy, “An Efficient Noise Cancellation Technique to remove noise from the ECG signal using Normalized Signed Regressor LMS algorithm”, IEEE International Conference on Bioinformatics and Biomedicine , 2009.
Mohammad Zia Ur Rahman, Rafi Ahamed Shaik, D V Rama Koti Reddy, ” Adaptive Noise Removal in the ECG using the Block LMS Algorithm” IEEE 2009.
Mohammad Zia Ur Rahman, Rafi Ahamed Shaik, D V Rama Koti Reddy, “Cancellation of Artifacts in ECG Signals using Sign based Normalized Adaptive Filtering Technique” IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Malaysia.
Monson Hayes H. “Statistical Digital Signal Processing and Modelling” – John Wiley & Sons 2002.
Nitish V. Thakor, Yi-Sheng Zhu, “Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection” IEEE Transactions on Biomedical Engineering. 18(8). August 1997.
S.C.Chan, Z.G.Zhang, Y.Zhou, and Y.Hu, “A New Noise-Constrained Normalized Least Mean Squares Adaptive Filtering Algorithm”, IEEE 2008.
Sachin singh and Dr K. L. Yadav ,” Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing” , International Journal on Computer Science and Engineering 02, (05), 2010.
Saeid Mehrkanoon, Mahmoud Moghavvemi, Hossein Fariborzi, “Real time ocular and facial muscle artifacts removal from EEG signals using LMS adaptive algorithm” International Conference on Intelligent and Advanced Systems, IEEE 2007.
Shivaram P. Arunachalam and Lewis F. Brown , “Real-Time Estimation of the ECG-Derived Respiration (EDR) Signal using a New Algorithm for Baseline Wander Noise Removal” 31st Annual International Conference of the IEEE EMBS Minneapolis,USA, September 2-6, 2009.
Slim Yacoub and Kosai Raoof, “Noise Removal from Surface Respiratory EMG Signal,” International Journal of Computer, Information, and Systems Science, and Engineering 2:4 2008.
Syed Zahurul Islam, Syed Zahidul Islam, Razali Jidin, Mohd. Alauddin Mohd. Ali, “Performance Study of Adaptive Filtering Algorithms for Noise Cancellation of ECG Signal”, IEEE 2009.
Yue-Der Lin and Yu Hen Hu , “ Power-Line Interference Detection and Suppression in ECG Signal Processing” IEEE Transactions on Biomedical Engineering, 55(1),January 2008.
Yunfeng Wu, Rangaraj M. Rangayyan,Ye Wu, and Sin-Chun Ng, “Filtering of Noise in Electrocardiographic Signals Using An Unbiased and Normalized Adaptive Artifact Cancellation System” Proceedings of NFSI & ICFBI 2007, Hangzhou, China, October 12-14, 2007.
Associate Professor A.Bhavani Sankar
- India
absankar72@gmail.com
Mr. D.Kumar
- India
Mr. K.Seethalakshmi
- India


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