Analysis of Human Body Balance Characteristics in Complex Network Based on Transfer Entropy
Wang Zheyuan1, Luo Zhizeng2*, Qiu Shenchen3
1(HDU-ITMO Joint Institute, Hangzhou Danzi University, Hangzhou 310018, China) 2(Institute of Intelligent Control and Robotics, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China) 3(The Fourth Affiliated Hospital Zhejiang University School of Medicine,Jinhua 322000, Zhejiang, China)
Abstract：In order to overcome the subjective defects of medical evaluation methods for human balance ability, an algorithm based on phase synchronization screening data and transfer entropy brain network was proposed. A total of 1 200 minutes of EEG balance data were collected from 20 subjects under 4 paradigms of occlusion of proprioception and vision. According to the physiological mechanism of human balance, the effective data segment of the balance adjustment process was screened through the phase synchronization. After data screening, 859 valid data segments were obtained, each with a length of 50~2 000 ms, and the brain function network model based on the transfer entropy was constructed from the screening results. A kind of characteristics was defined that can reflect the reception of human balance information and cooperative processing of motion perception. The 10-fold cross-validation in the same batch of experimental data showed that the new feature improved the classification accuracy of the 4 paradigms to 73.63%, which was higher than that of other brain functional network features (56.23%) and center of pressure (COP) features (67.90%). According to the analysis of synchronization, it is concluded that the visual system plays a major role in the adjustment of human body balance. The application of the new balance features has significantly improved the accuracy of the classification.
王哲远, 罗志增, 裘晟晨. 基于传递熵的复杂网络人体平衡特征分析[J]. 中国生物医学工程学报, 2022, 41(2): 159-166.
Wang Zheyuan, Luo Zhizeng, Qiu Shenchen. Analysis of Human Body Balance Characteristics in Complex Network Based on Transfer Entropy. Chinese Journal of Biomedical Engineering, 2022, 41(2): 159-166.
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