Paper Info
Title
Revealing Control Mechanism From Multifractal Analysis On Physiological Signals
Abstract
Multifractal theory has been widely used in various fields of research study. In this paper, methods were proposed to extract the multifractal descriptors of physiological signals from kinematic measurement of cervical spine region during postural sway when static sitting at upright position. The analysis is based on the multifractal detrended fluctuation analysis. The proposed multifractal parameters can be well described by variation space among the experimental subject group through acquisition of trials. Various analytical aspects of experiments have been conducted to verify the robustness and confidence of the proposed motor control mechanism. The exhibition of multifractality structure is hypothesized in describing various discharge of neural activity on motor control in order to balance the static posture through body sway. Variation on the long-range correlated structure can be found among subject groups. This is suggested as the reflection on coordinated behavior in the presence of external variation or pathological conditions. Both impersistent and persistent structures are observed in the multifractal spectrums from experiment. This reveals the relationship to the local and global neural interconnectivity, in which time scales can reflect local and progressively longer neighborhoods of neural interaction, within and outside the given spinal region. Results demonstrate that control mechanism can be revealed and knowledge discovered by means of the multifractal analysis and the extracted descriptors.
Year
Venue
Keywords
2015
2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
motor control, detrended fluctuation analysis, multifractal analysis, noise, spinal curvature, biomechanics, physiological signal
Field
DocType
Citations 
Kinematics,Pattern recognition,Computer science,Balance (ability),Neural activity,Motor control,Robustness (computer science),Cervical spine,Detrended fluctuation analysis,Artificial intelligence,Multifractal system
Conference
0
PageRank 
References 
Authors
0.34
2
3
Name
Order
Citations
PageRank
Newman Lau141.58
Clifford Sze-tsan Choy2758.03
Daniel H K Chow3112.20