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Complexity analysis of stride interval time series by threshold dependent symbolic entropy
1Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan.
European Journal of Applied Physiology
|July 15, 2006
Summary
Symbolic entropy analysis reveals distinct gait complexity differences between healthy individuals and those with neurodegenerative diseases. This non-invasive method quantifies gait rhythm, aiding in understanding neurological impairments.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Human Physiology
Background:
- Human gait exhibits complex temporal fluctuations reflecting locomotor system rhythm.
- Stride interval variability offers a non-invasive method to assess neurological impairments.
- Age and disease alter gait patterns, necessitating sensitive analytical techniques.
Purpose of the Study:
- To apply threshold-dependent symbolic entropy to analyze gait complexity in control and neurodegenerative disease subjects.
- To quantitatively characterize gait complexity using symbolic nonlinear time series analysis.
- To differentiate gait patterns between healthy and neurodegenerative populations.
Main Methods:
- Utilized threshold-dependent symbolic entropy based on symbolic nonlinear time series analysis.
- Calculated normalized corrected Shannon entropy (NCSE) from stride interval time series.
- Compared complexity measures between control subjects and individuals with neurodegenerative diseases.
Main Results:
- Symbolic entropy demonstrated significant differences in gait complexity between control and neurodegenerative disease groups within specific threshold ranges.
- The complexity of physiological gait signals exceeded that of random signals at short threshold values.
- Normalized corrected Shannon entropy effectively quantified gait complexity.
Conclusions:
- Threshold-dependent symbolic entropy is a valuable tool for distinguishing gait complexity in neurodegenerative diseases.
- This method provides a sensitive, non-invasive approach to evaluate gait alterations due to neurological conditions.
- Gait complexity analysis can aid in early detection and monitoring of neurodegenerative disorders.
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