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

• 作者: Behzad Bozorgtabar Hamed Azami Karim Mohammadi
• 来源期刊: 信号与信息处理(英文)
• 学科: 医学
• 关键词: NON-STATIONARY Signal Adaptive SEGMENTATION Modified Varri MOVING AVERAGE (MA) FILTER Sa-vitzky-Golay FILTER
• 年，卷（期）: 2012,（1）
• 页码范围: 39-44
• 页数: 6页
• 分类号: R73

摘要

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.