An effective algorithm for quick fractal analysis of movement biosignals

The problem of numerically classifying patterns, of crucial importance in t he biomedical field, is here faced by means of their fractal dimension. A n ew simple algorithm was developed to characterize biomedical mono-dimension al signals avoiding computationally expensive methods, generally required b y the classical approach of the fractal theory. The algorithm produces a nu mber related to the geometric behaviour of the pattern providing informatio n on the studied phenomenon. The results are independent of the signal ampl itude and exhibit a fractal measure ranging from I to 2 for monotonically g oing-forwards monodimensional curves, in accordance with theory. Accurate c alibration and qualification were accomplished by analysing basic waveforms . Further studies concerned the biomedical field with special reference to gait analysis: so far, well controlled movements such as walking going up, and downstairs and running, have been investigated. Controlled conditions o f the test environment guaranteed the necessary repeatability and the accur acy of the practical experiments in setting up the methodology. The algorit hm showed good performance in classifying the considered simple movements i n the selected sample of normal subjects. The results obtained encourage us to use this technique for an effective on-line movement correlation with o ther long-term monitored variables such as blood pressure, EGG, etc.