Most suitable mother wavelet for the analysis of fractal properties of stride interval time series via the average wavelet coefficient method

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• 作者：Zhenwei Zhang^a ; Jessie VanSwearingen^b ; Jennifer S. Brach^b ; Subashan Perera^c ; Ervin Sejdić^a ; ^{esejdic@ieee.org}
• 关键词：Fractals ; Time series analysis ; Self-similarity ; Gait ; Stride intervals ; Wavelets ; 1/f process ; Mother wavelet
• 刊名：Computers in Biology and Medicine
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：80
• 期：Complete
• 页码：175-184
• 全文大小：1339 K
• 卷排序：80

文摘

Human gait is a complex interaction of many nonlinear systems and stride intervals exhibiting self-similarity over long time scales that can be modeled as a fractal process. The scaling exponent represents the fractal degree and can be interpreted as a “biomarker” of relative diseases. The previous study showed that the average wavelet method provides the most accurate results to estimate this scaling exponent when applied to stride interval time series. The purpose of this paper is to determine the most suitable mother wavelet for the average wavelet method. This paper presents a comparative numerical analysis of 16 mother wavelets using simulated and real fractal signals. Simulated fractal signals were generated under varying signal lengths and scaling exponents that indicate a range of physiologically conceivable fractal signals. The five candidates were chosen due to their good performance on the mean square error test for both short and long signals. Next, we comparatively analyzed these five mother wavelets for physiologically relevant stride time series lengths. Our analysis showed that the symlet 2 mother wavelet provides a low mean square error and low variance for long time intervals and relatively low errors for short signal lengths. It can be considered as the most suitable mother function without the burden of considering the signal length.