Search for Articles
International Journal of Clinical Medicine and Bioengineering
ISSN:2737-534X
Frequency: Quarterly Published by lIKll


Open Access Research Paper
 IJCMB 2022/03
Vol.2, Iss.1 : 8-15
https://doi.org/10.35745/ijcmb2022v02.01.0002

Ordinal Pattern-Based Dissimilarity Measure for Slow Cortical Potential Training in Stroke


Wenbin Shi1*, Chuting Zhang,1, Chien-Hung Yeh1 and Heng Liu1

1School of Information and Electronics, Beijing Institute of Technology, Beijing, China

Received:N/A; Revised:N/A; Accepted:N/A; Published:March 30, 2022
Abstract:
Biological signals, such as EEG and ECG, generate complex fluctuations in correspondence with the underlying system dynamics. In this study, we propose a dissimilarity quantification, which is an improvement of information-based similarity for capturing the features of underlying dynamics from positivity or negativity trials in the neurofeedback training of chronic stroke patients. Simulated Gaussian white and pink noises are used to evaluate the validity of this measure by different embedding dimensions, time delays, and data lengths. Then, the method is applied to slow cortical potentials of chronic stroke patients. The results imply that the proposed dissimilarity measure characterizes the unique dynamical patterns of SCP signals. The dissimilarity measure is capable of capturing the underlying dynamics of SCPs that belong to positivity or negativity trials. Besides, as the session progressed, the dissimilarity showed an increasing trend.

Keywords:  Information-based similarity, Ordinal pattern, Stroke, Slow Cortical Potentials

Download PDF
*Corresponding author; e-mail: swb1123@163.com


Citation:Shi, W.; Zhang,, C.; Yeh, C.H.; Liu, H.Ordinal Pattern-Based Dissimilarity Measure for Slow Cortical Potential Training in Stroke. International Journal of Clinical Medicine and Bioengineering 2022, 2, 8-15. https://doi.org/10.35745/ijcmb2022v02.01.0002

86 Views 65 Downloads

Copyright: © 2022  The Author(s). Published with license by IIKII, Singapore. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Back