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| Using Approximate Entropy to Modular Process and Analyze EOG Signals of Autism Spectrum Disorder |
| Lin Dong1*, Qi Shiyi1, Yuan Ding2, Zhang Sijia1, Zhuang Wanyu3, Lin lili1, Li Yurong2 |
1(Department of Acupuncture and Massage, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China)
2(Department of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)
3(Department of Child Healthcare ,Fujian Maternity and Childcare Hospital,Fuzhou 350001, China) |
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[3] 徐晓英,随力,赵彦富,等. 基于眼电信号的人机交互系统研究进展[J]. 中国医学物理学杂志,2020,37(5):629-634.
[4] 林栋, 吴江昀, 陈采益, 等. 一种基于生物反馈技术的自闭症穴位刺激治疗仪: ZL201510956631.5[P]. 2018-02-02.
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[6] 林栋,林丽莉. 一种眼电信号捡拾与优化分析方法及装置: ZL202110117521.5[P]. 2022-10-28.
[7] Pincus SM. Approximate entropy as a measure of system complexity[J]. Proc Natl Acad Sci,1991, 88(6): 2297-2301.
[8] Na Ji, Liang Ma, Hui Dong, et al. EEG signals feature extraction based on DWT and EMD combined with approximate entropy[J]. Brain Sciences,2019,9(8):1-14.
[9] Liu Yaya, Xu Lingyu, Li Jun, et al. Attentional connectivity-based prediction of autism using heterogeneous rs-fMRI data from CC200 atlas[J]. Exp Neurobiol,2020,29(1):27-37.
[10] Qi Shiyi, Lin Dong, Lin Lili, et al. Using nonlinear dynamics and multivariate statistics to analyze EEG signals of insomniacs with the intervention of superficial acupuncture[J]. Evidence-Based Complementary and Alternative Medicine,2020,2020:1-13. |
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