Adaptive Segmentation with Optimal Window Length Scheme using Fractal Dimension and Wavelet Transform




In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different frequency bands using wavelet transform. The fractal dimension of the decomposed signal is calculated in a sliding window and the results are used as a feature for adaptive segmentation. A criterion is introduced in this paper to choose a proper length for the sliding window. Performance of the proposed method is compared with that of three other existing segmentation methods using synthetic and real EEG data. Simulation results show the high efficiency of the proposed method in signal segmentation.