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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.

has been cited by the following article:

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

  AUTHORS: Hamed Azami, Karim Mohammadi, Behzad Bozorgtabar

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

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.3 No.1, February 28, 2012

  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.