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

Hamed Azami, Karim Mohammadi, Behzad Bozorgtabar

Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran..

DOI: 10.4236/jsip.2012.31006                

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.

Keywords

Non-Stationary Signal; Adaptive Segmentation; Modified Varri; Moving Average (MA) Filter; Sa-vitzky-Golay Filter

Conflicts of Interest

The authors declare no conflicts of interest.

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