A comparison of waveform fractal dimension algorithms

The fractal dimension of a waveform represents a powerful tool for transien t detection, In particular, in analysis of electroencephalograms and electr ocardiograms, this feature has been used to identify and distinguish specif ic states of physiologic function, ii variety of algorithms are available f or the computation of fractal dimension. In this study, the most common met hods of estimating the fractal dimension of biomedical signals directly in the time domain (considering the time series as a geometric object) are ana lyzed and compared. The analysis is performed over both synthetic data and intracranial electroencephalogram data recorded during presurgical evaluati on of individuals with epileptic seizures. The advantages and drawbacks of each technique are highlighted. The effects of window size, number of overl apping points, and signal-to-noise ratio are evaluated for each method. Thi s study demonstrates that a careful selection of fractal dimension algorith m is required for specific applications.