Multiscale fractal dimension technique for characterization and analysis of biomedical signals

In this paper, we have proposed multiscale fractal dimension (MSFD) technique to characterize signals at multiple time scales. In this technique, multiple scales of the signal are obtained by segment averaging and the complexity of the resulting signals at those scales is quantified using multiresolution area-based fractal dimension measure. The technique is applied to intracranial EEG records and meditation HRV signals to detect change in states of physiological systems. We have considered two types of meditation techniques and pre-meditation state is used as control state against which MSFD parameters are group matched. The proposed MSFD technique has provided good performance and statistically significant results in discriminating epileptic seizures and meditation states from corresponding controls. The technique can be used in diverse applications of signal processing.