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

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

Journal: Advances in Molecular Imaging
ISSN 2161-6728

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

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.

Tango Jona
Tangokurs Rapperswil-Jona

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