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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
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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.
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Keywords: Information-based similarity, Ordinal pattern, Stroke, Slow Cortical Potentials
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Received:N/A; Revised:N/A; Accepted:N/A; Published:March 30, 2022
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*Corresponding author; e-mail: swb1123@163.com
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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
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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.
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