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

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Author(s): Hamed Azami | Karim Mohammadi | Behzad Bozorgtabar

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 03;
Issue: 01;
Start page: 39;
Date: 2012;
Original page

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

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.
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