In this paper, a method for grading Parkinson's disease patients based on the analysis of the complexity of electrostatic gait signals is proposed. The non-contact electrostatic sensing device is used to obtain the electrostatic gait signals generated by the natural stepping process of Parkinson's patients and healthy people, and extract their features respectively. The LZ complexity, C0 complexity, multi-scale entropy (MSE) and permutation entropy (PE) were obtained, and the differences in gait characteristics of the two groups were compared and analyzed. Then, the support vector machine (SVM) algorithm was used to establish the Parkinson's disease grading model. The experimental results show that several complex features proposed in this paper can effectively distinguish Parkinson's patients with different severity levels, and the classification accuracy rate is 91.77%.
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