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| 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) |
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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.
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Received: 21 December 2020
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Corresponding Authors:
*E-mail: luo@hdu.edu.cn
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| About author:: #Member, Chinese Society of Biomedical Engineering |
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[1] 苏海龙,许兆健,张峻霞,等. 人体步态滑跌过程中的下肢表面肌电特性研究[J]. 中国生物医学工程学报,2017,36(2):233-237.
[2] 韩俊,罗志增,张启忠. 基于静态姿势图的人体平衡功能检测与评估[J]. 中国生物医学工程学报,2014,33(5):539-545.
[3] 肖金壮,睢少坤,王洪瑞,等. 基于随机视觉刺激的人体平衡能力评测系统研究[J]. 中国生物医学工程学报, 2014,33(4):498-502.
[4] Wu Chenjung, Kao Tungwei, Chen Yuanyu, et al. Examining the association between vestibular function and lower extremity circumference in an aged population [J]. Geriatrics & Gerontology International, 2019, 19(7): 622-627.
[5] Yang Feng, Liu Xinyue. Relative importance of vision and proprioception in maintaining standing balance in people with multiple sclerosis[J]. Multiple Sclerosis and Related Disorders, 2020, 39(2): 322-330.
[6] Mckay JL, Ting LH, Hackney ME. Balance, body motion, and muscle activity after high-volume short-term dance-based rehabilitation in persons with Parkinson disease: a pilot study [J]. Journal of Neurological Physical Therapy, 2016, 40: 257-268.
[7] Puntkattalee MJ, Whitmire CJ, Macklin AS, et al. Directional acuity of whole-body perturbations during standing balance[J]. Gait & Posture, 2016, 48(5): 77-82.
[8] Masakado Y, Ushiba J, Tsutsumi N, et al. EEG-EMG coherence changes in postural tasks[J]. Electromyography and Clinical Neurophysiology: International Bimonthly Review, 2008, 48: 27-33.
[9] 金晟, 罗志增, 严志华. 基于足底压力中心和脑肌电相干性特征的人体平衡能力评估方法[J].航天医学与医学工程,2020,33(1):52-58.
[10] Akash UB, Kylie KH, Adam MB, et al. Muscle strength, not age, explains unique variance in echo intensity[J]. Experimental Gerontology, 2020, 139(7): 149-157.
[11] Mario CS, Katharina P, Wioleta W, et al. Neurophysiological evidence for evaluative feedback processing depending on goal relevance[J]. NeuroImage, 2020, 215(10): 39-45.
[12] Roelfsema PR, Engel AK, König P, et al. Visuomotor integration is associated with zero time-lag synchronization among cortical areas[J]. Nature, 1997, 385(6612): 157-161.
[13] Hedayetul IS, Nanda N, Ramasamy V, et al. Directed connectivity analysis of functional brain networks during cognitive activity using transfer entropy[J]. Neural Processing Letters, 2017, 45(3): 807-824.
[14] Chaieb K, Leszczynski T, Axmacher H, et al. Theta-gamma phase-phase coupling during working memory maintenance in the human hippocampus[J]. Cognitive Neuroscience, 2015, 6: 149-157.
[15] Yanping R, Liping P, Xueyun D, et al. Theta oscillation and functional connectivity alterations related to executive control in temporal lobe epilepsy with comorbid depression[J]. Clinical Neurophysiology, 2020, 131(7): 1599-1609.
[16] 高云园,任磊磊,周旭,等. 基于变尺度符号传递熵的多通道脑肌电信号耦合分析[J]. 中国生物医学工程学报,2018,37:8-16.
[17] 肖金壮,齐佳龙,王洪瑞. 基于小波分析的人体平衡指标提取方法研究[J]. 中国生物医学工程学报,2012,31:507-511.
[18] 牛小辰,陈晓玲,陈迎亚,等. 基于格兰杰因果性的行走状态下脑肌电同步分析[J]. 中国生物医学工程学报,2014,33(6):696-706. |
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