Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson's disease: stitching together short gait trials.
Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., [Formula: see text][Formula: see text] strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ([Formula: see text] strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents [Formula: see text] (DFA) and [Formula: see text] (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning [Formula: see text] and [Formula: see text]. Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true [Formula: see text]. The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.
Faculty of Health and Social Sciences, Hochschule Fresenius, University of Applied Sciences, Idstein, Germany.
Marietta
M
Kirchner
Faculty of Health and Social Sciences, Hochschule Fresenius, University of Applied Sciences, Idstein, Germany.
Patric
P
Schubert
Faculty of Health and Social Sciences, Hochschule Fresenius, University of Applied Sciences, Idstein, Germany.
Magnus
M
Liebherr
Faculty of Health and Social Sciences, Hochschule Fresenius, University of Applied Sciences, Idstein, Germany.
Christian T
CT
Haas
D000368:Aged; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D017709:Fractals; D005684:Gait; D020233:Gait Disorders, Neurologic / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D010300:Parkinson Disease / Q000150:complications* / Q000503:physiopathology; D012984:Software; D018709:Statistics, Nonparametric; D016138:Walking; D055815:Young Adult