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.