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Usage of Fractal Analysis in Evaluating Health and Disease

Kimberly Edginton, MS and Necip Berme, PhD
Department of Mechanical Engineering, The Ohio State University, Columbus, Ohio, USA

In recent years developments in the field of non-linear dynamics have led to new methods of evaluating physiological signals. One promising method is the use of fractal analysis. Fractal analysis can be used to characterize patterns occurring in any data known to exhibit self-similar patterns. These self-similar patterns are indicated by the repetition of trends in the data series, such as persistent patterns in which current data continues to follow the increasing or decreasing trends set by previous data in the same data set. A number of physiological biosignals have been found to exhibit fractal-like patterns, including: heart rate, blood pressure, EEG potentials, stride interval, and center of pressure displacement. Fractal analysis has proven a promising tool in differentiating healthy from diseased function. Fractal analysis results in a quantitative measure, known as a fractal dimension, to describe the self-similar patterns observed in time-series data. Changes in the fractal dimension can represent changes in health. Such changes have been related to Parkinson’s disease tremor, obstructive sleep apnea, epilepsy, fetal alcohol syndrome, and even sudden cardiac arrest. The aim of this study was to determine if fractal analysis could be applied to center of pressure measurements to differentiate the postural sway patterns of healthy young individuals, healthy elderly individuals, and individuals with Parkinson’s disease. It was hypothesized that the postural sway patterns, as characterized by the fractal dimension, would be significantly different for those of advanced age and disease.


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