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This paper provides a comprehensive review of nonlinear methods and algorithmic modeling for time series analysis, with applications to physiological systems and dynamic diseases.
This review outlines the theoretical concepts of nonlinear dynamic systems and their application to algorithmic modeling of biological time series such as EMG and posturography.
Nonlinear methods may aid physiological signal analysis; leaves open clinical adoption pending prospective validation.
The first part of this Review describes a few of the main methods that have been employed in non-linear time series analysis with special reference to biological applications (biomechanics). The second part treats the physical basis of posturogram data (human balance) and EMG (electromyography, a measure of muscle activity).
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Frank Borg (2005) reported a review. Nonlinear methods and modelling was evaluated. This paper provides a comprehensive review of nonlinear methods and algorithmic modeling for time series analysis, with applications to physiological systems and dynamic diseases.
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