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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 11    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 11, November 2011
Removal of Baseline Fluctuation From Emg Recordings
Full Text(PDF, 3000)  PP.  
Author(s)
Sadhana Pal, Gyan Prakash Pal, Manish, Anupum Bhardwaj
KEYWORDS
EMG, Baseline removal, MUAPs
ABSTRACT
Appropriate cancellation of the baseline fluctuation (BLF) is an important issue when recording EMG signals as it may degrade signal quality and distort qualitative and quantitative analysis. We present statistical and filter-design approach for cancellation of the BLF based on several signal processing techniques used sequentially. The methodology is to estimate the spectral contents of the BLF, and then to use this estimation to design a high pass Butterworth filter by using Bilinear Transformation that cancel the BLF present in the signal. Two merit figures are devised for measuring the degree of BLF present in an EMG record. These figures are used to compare both methods which naively consider the baseline without any fluctuation i.e. constant potential shift. Applications of the techniques on real and simulated EMG signals show the superior performance of our approach in terms of both visual inspection and the merit figures.
References
[1] Christodoulos I. Christodoulou, Constantinos S. Pattichis. “A New Technique for the Classification and Decomposition of EMG Signals” Christodoulou, C.I.; Pattichis, C.S.; Neural Networks, 1995. Proceeding, IEEE International Conference 1996.

[2] C.s. Pattichis, A. Elia, C.N. Schizas, “Classification performance of motor unit action potential features” , 1994 IEEE, pp 1338- 1339.

[3] Craven, P. and Grace Wahba, (1979), Smoothing Noisy Data with Spline Functions: Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-Validation"", pp 377-403.

[4] C.S. Burrus, J.H. McClellan, A.V. Oppenheim, T.W. Parks, R.W. Schafer & H.W. Schussler. Computer-Based Exercises for Signal Processing Using MATLAB. Prentice-Hall, 1994.

[5] D.C. Preston, B.E. Shapiro, Electromyography and Neuromuscular Disorders, Butterworth-Heinemann, Boston, 1998.

[6] E. Stalberg, S. Andreassen, B. Falk, H. Lang, A. Rosenfalk, W. Trojaborg, Quantitative analysis of individual motor units potentials: a proposition for standardized terminology and criteria for measurement, J. Clin. Neurophysiol. (1986), pp 313–348.

[7] E. Stalberg, M.Sonno, assessment of variability in the shape of MUAP, the “jiggle”, at consecutive discharges.

[8] Emmanuel C. Ifeachor & Barrie W. Jervis “Digital signal processing a Practical Approach” second edition (Publisher: Pearson Education Asia).

[9] I.Rodriguez-Carreno, A. Malanda Trigueros proposed an approach for the segmentation of EMG signal, (2006).

[10] J. V. Basmajian and C.J.Deluca, “Muscles Alive, Their Functions Revealed by Electromyography”, Williams & Wilkins, Baltimore, fifth edition, (1985)

[11] Jhnjun Fang, Bhagwan T. Shahan, Francis “Estimation of single motor unit discharges” pattern based on fractional process on patient, (1995),pp 1327-132

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