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


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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