An Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of the decomposed signal is computed and used as a feature for adaptively segmenting the signal. Any changes on the signal amplitude or frequency are reflected on the fractal dimension of the signal. The proposed method was applied on a synthetic signal and real EEG to evaluate its performance on segmenting non-stationary signals. The results indicate that the proposed approach outperforms the existing method in signal segmentation.