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Use of information entropy measures of sitting postural sway to quantify developmental delay in infants
Joan E Deffeyes1, Regina T Harbourne, Stacey L DeJong
1Nebraska Biomechanics Core Facility, University of Nebraska at Omaha, Omaha, NE, 68182, USA. jdeffeyes@mail.unomaha.edu
Insights
Infants with developmental delay show less complex postural sway. Optimized entropy analysis effectively distinguishes delayed from typical infant development, aiding early detection.
Area of Science:
- Biomedical Engineering
- Developmental Pediatrics
- Motor Control Research
Background:
- Information entropy quantifies postural sway complexity.
- Assesses motor control in various populations.
- Provides insight into pathological motor function.
Purpose of the Study:
- Assess infant motor control development using postural sway.
- Analyze sitting postural sway data in infants.
- Differentiate between typical and delayed motor development.
Main Methods:
- Utilized force plate center of pressure measurements.
- Applied symbolic entropy and approximate entropy measures.
- Optimized parameters for infant sitting postural sway data.
Main Results:
- Asymmetric symbolic entropy provided the widest population separation.
- Optimized approximate entropy also performed well.
- Delayed infants exhibited less medial-lateral sway complexity and altered left-right symmetry.
Conclusions:
- Optimized entropy algorithms significantly improve infant developmental delay detection.
- Enhanced analysis aids in distinguishing infants with delayed motor function.
- This method offers a valuable tool for early identification of developmental issues.
Background:
By quantifying the information entropy of postural sway data, the complexity of the postural movement of different populations can be assessed, giving insight into pathologic motor control functioning.
Methods:
In this study, developmental delay of motor control function in infants was assessed by analysis of sitting postural sway data acquired from force plate center of pressure measurements. Two types of entropy measures were used: symbolic entropy, including a new asymmetric symbolic entropy measure, and approximate entropy, a more widely used entropy measure. For each method of analysis, parameters were adjusted to optimize the separation of the results from the infants with delayed development from infants with typical development.
Results:
The method that gave the widest separation between the populations was the asymmetric symbolic entropy method, which we developed by modification of the symbolic entropy algorithm. The approximate entropy algorithm also performed well, using parameters optimized for the infant sitting data. The infants with delayed development were found to have less complex patterns of postural sway in the medial-lateral direction, and were found to have different left-right symmetry in their postural sway, as compared to typically developing infants.
Conclusion:
The results of this study indicate that optimization of the entropy algorithm for infant sitting postural sway data can greatly improve the ability to separate the infants with developmental delay from typically developing infants.
