基于动态加权符号互信息与k均值聚类的帕金森病患者静息脑电关联状态识别

Resting-state electroencephalogram relevance state recognition of Parkinson’s disease based on dynamic weighted symbolic mutual information and k-means clustering

目前,帕金森病发生率在逐渐上升,严重影响着患者生活质量,社会诊疗负担不断加重。然而,该病的早期监测手段有限,很难及时干预。为了发现其标志物,本文对服药前后帕金森病患者及健康人的32通道静息态脑电数据进行分频段研究。首先利用动态加权符号互信息计算各通道脑电信号间相关性,再通过k均值聚类实现信号关联矩阵的分类,最后得到脑电信号关联状态。通过统计分析发现,在Beta频段(P=0.034)与Gamma频段(P=0.010)各有一个脑电信号关联状态可显著区分未服药帕金森病患者与健康人。这表明未服药帕金森病患者与健康人的静息态脑电各通道信号相关性差异有统计学意义。而在服药与未服药帕金森病患者、服药帕金森病患者与健康人之间,其关联状态差异均没有统计学意义。这可为帕金森病的临床诊断提供一种参考。

At present, the incidence of Parkinson’s disease(PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram(EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band(P = 0.034) and State5 of Gamma frequency band(P = 0.010) could be used to differentiate health controls and offmedication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.