Complexity of Postural Control in Infants: Linear and Nonlinear Features Revealed by Principal Component Analysis

被引:0
|
作者
Harbourne, Regina T. [1 ]
Deffeyes, Joan E. [1 ]
Kyvelidou, Anastasia [1 ]
Stergiou, Nicholas [1 ]
机构
[1] Univ Nebraska, Omaha, NE 68182 USA
关键词
Infants; postural control; complexity; nonlinear; principal component analysis; APPROXIMATE ENTROPY; PARKINSONS-DISEASE; CEREBRAL-PALSY; TIME-SERIES; HEART-RATE; FEATURE-SELECTION; STANDING BALANCE; SWAY; CHILDREN; VARIABILITY;
D O I
暂无
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
Nonlinear analysis of standing postural control in healthy adults reveals a chaotic structure of the center of pressure time series. Independent sitting is the first controlled posture during development, and can also be examined for nonlinear dynamics. We performed a principal component analysis on variables extracted from the center of pressure (COP) time series of infants sitting independently. Our put-pose was to describe factors that could be intetpreted for clinical use in evaluating postural control for infants, and determine if nonlinear measures provide additional information about postural control not quantified by standard linear measures. Four factors were identified: the area or amount of postural sway and the overall variability of the sway (linear); the complexity of the sway in the anterior-posterior direction (nonlinear); power variability or velocity (linear); and the complexity of the sway in the medial-lateral direction (nonlinear). Nonlinear measures, which are used to examine complexity in many physiological systems, describe the variability of postural control that is not described by linear measures. Nonlinear measures may be critical in determining the developing health of the postural control system in infants, and may be useful in early diagnosis of movement disorders. The measurement of nonlinear dynamics of postural control reveals a chaotic structure of postural control in infancy, which may he an indicator of healthy postural control throughout development.
引用
收藏
页码:123 / 144
页数:22
相关论文
共 50 条
  • [1] Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis
    Nguyen, Phuong H.
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2006, 65 (04) : 898 - 913
  • [2] Evaluation of the Complexity of Control of Simple Linear Hand Movements Using Principal Component Analysis
    A. V. Gorkovenko
    O. V. Lehedza
    T. I. Abramovych
    W. Pilewska
    V. S. Mischenko
    M. Zasada
    Neurophysiology, 2019, 51 : 132 - 140
  • [3] Evaluation of the Complexity of Control of Simple Linear Hand Movements Using Principal Component Analysis
    Gorkovenko, A., V
    Lehedza, O., V
    Abramovych, T., I
    Pilewska, W.
    Mischenko, V. S.
    Zasada, M.
    NEUROPHYSIOLOGY, 2019, 51 (02) : 132 - 140
  • [4] Applicability of linear and nonlinear principal component analysis for damage detection
    Santos, A. D. F.
    Silva, M. F. M.
    Sales, C. S.
    Costa, J. C. W. A.
    Figueiredo, E.
    2015 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2015, : 869 - 874
  • [5] Linear and nonlinear analysis of postural control in frailty syndrome
    de Vassimon-Barroso, Verena
    Catai, Aparecida Maria
    de Souza Buto, Marcele Stephanie
    Porta, Alberto
    De Medeiros Takahashi, Anielle Cristhine
    BRAZILIAN JOURNAL OF PHYSICAL THERAPY, 2017, 21 (03) : 184 - 191
  • [6] Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring
    Ines Jaffel
    Okba Taouali
    Mohamed Faouzi Harkat
    Hassani Messaoud
    The International Journal of Advanced Manufacturing Technology, 2017, 88 : 3265 - 3279
  • [7] Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring
    Jaffel, Ines
    Taouali, Okba
    Harkat, Mohamed Faouzi
    Messaoud, Hassani
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (9-12): : 3265 - 3279
  • [8] Robust principal component analysis: A factorization-based approach with linear complexity
    Peng, Chong
    Chen, Yongyong
    Kang, Zhao
    Chen, Chenglizhao
    Cheng, Qiang
    INFORMATION SCIENCES, 2020, 513 : 581 - 599
  • [9] Nonlinear constrained principal component analysis in the quality control framework
    Gallo, Michele
    D'Ambra, Luigi
    DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS, 2008, : 193 - +
  • [10] Principal component analysis for nonlinear model reference adaptive control
    McLain, RB
    Henson, MA
    COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (01) : 99 - 110