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An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter

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DOI: 10.4236/jsip.2012.31006    7,964 Downloads   13,430 Views   Citations

ABSTRACT

Analysis of long-term EEG signals needs that it be segmented into pseudo stationary epochs. That work is done by regarding to statistical characteristics of a signal such as amplitude and frequency. Time series measured in real world is frequently non-stationary and to extract important information from the measured time series it is significant to utilize a filter or smoother as a pre-processing step. In the proposed approach, the signal is initially filtered by Moving Average (MA) or Savitzky-Golay filter to attenuate its short-term variations. Then, changes of the amplitude or frequency of the signal is calculated by Modified Varri method which is an acceptable algorithm for segmenting a signal. By using synthetic and real EEG data, the proposed methods are compared with original approach (simple Modified Varri). The simulation results indicate the absolute advantage of the proposed methods.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Azami, K. Mohammadi and B. Bozorgtabar, "An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter," Journal of Signal and Information Processing, Vol. 3 No. 1, 2012, pp. 39-44. doi: 10.4236/jsip.2012.31006.

References

[1] H. Azami, K. Mohammadi and H. Hassanpour “An Improved Signal Segmentation Method Using Genetic Algorithm,” International Journal of Computer Applications, Vol. 29, No. 8, 2011, pp. 5-9.
[2] H. Hassanpour and M. Shahiri, “Adaptive Segmentation Using Wavelet Transform,” International Conference on Electrical Engineering, Lahore, 11-12 April 2007, pp. 1-5. doi:10.1109/ICEE.2007.4287348
[3] L. Wong and W. Abdulla, “Time-Frequency Evaluation of Segmentation Methods for Neonatal EEG Signals,” IEEE International Conference on Engineering in Medicine and Biology Society, New York, 1-3 September 2006, pp. 1303-1306. doi:10.1109/IEMBS.2006.259472
[4] S. M. Anisheh and H. Hassanpour, “Adaptive Segmentation with Optimal Window Length Scheme Using Fractal Dimension and Wavelet Transform,” International Journal of Engineering, Vol. 22, No. 3, 2009, pp. 257-268.
[5] H. Hassanpour, M. Mesbah and B. Boashash, “Time-Frequency Based Newborn EEG Seizure Detection Using Low and High Frequency Signatures,” Physiological Measurement, Vol. 25, No. 4, 2004, pp. 935-944. doi:10.1088/0967-3334/25/4/012
[6] H. Hassanpour, M. Mesbah and B. Boashash, “Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques,” EURASIP Journal on Applied Signal Processing, Vol. 2004, No. 16, 2004, pp. 2544-2554. doi:10.1155/S1110865704406167
[7] K. Kosar, L. Lhotska, and V. Krajca, “Classification of Long-Term EEG Recordings,” Lecture Notes in Computer Science, Vol. 3337, 2004, pp. 322-332. doi:10.1007/978-3-540-30547-7_33
[8] M. E. Kirlangic, D. Perez, S. Kudryavtseva, G. Griessbach, G. Henning and G. Ivanova, “Fractal Dimension as a Feature for Adaptive Electroencephalogram Segmentation in Epilepsy,” IEEE International Conference on Engineering in Medicine and Biology Society, Vol. 2, 2001, pp. 1573-1576.
[9] D. Wang, R. Vogt, M. Mason and S. Sridharan, “Automatic Audio Segmentation Using the Generalized Likelihood Ratio,” 2nd IEEE International Conference on Signal Processing and Communication Systems, Gold Coast, 15-17 December 2008, pp. 1-5. doi:10.1109/ICSPCS.2008.4813705
[10] J. Lv, X. Li and T. Li, “Web-Based Application for Traffic Anomaly Detection Algorithm,” Second IEEE International Conference on Internet and Web Applications and Services, Morne, 13-19 May 2007, pp. 44-60. doi:10.1109/ICIW.2007.72
[11] R. Agarwal and J. Gotman, “Adaptive Segmentation of Electroencephalographic Data Using a Nonlinear Energy Operator,” IEEE International Symposium on Circuits and Systems, Vol. 4, 1999, pp. 199-202.
[12] J. Gao, H. Sultan, J. Hu, and W. W. Tung, “Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison,” IEEE Signal Processing Letters, Vol. 17, No. 3, 2010, pp. 237-240. doi:10.1109/LSP.2009.2037773
[13] A. Savitzky and M. J. E. Golay, “Smoothing and Differentiation of Data by Simplified Least Square Procedure,” Analytic Chemistry, Vol. 36, No. 8, 1964, pp. 1627-1639. doi:10.1021/ac60214a047
[14] J. Lue, K. Ying and J. Bai, “Savitzky-Golay Smoothing and Differentiation Filter for Even Number Data,” Signal Processing, Vol. 85, No. 7, 2005, pp. 1429-1434. doi:10.1016/j.sigpro.2005.02.002
[15] H. Hassanpour, “A Time-Frequency Approach for Noise Reduction,” Digital Signal Processing, Vol. 18, No. 5, 2008, pp. 728-738. doi:10.1016/j.dsp.2007.09.014

  
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