基于脑部电阻抗信息的非线性动力学脑卒中诊断方法
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摘要：
缺血性脑卒中因其较高的发病率和致残率严重威胁人们的健康.作为缺血性脑卒中病情的诊断手段之一,电阻抗层析成像(Electrical Impedance Tomography,EIT)具有便携、无创、连续监测、低成本的优点.然而,现有EIT研究较多关注单帧检测信息条件下病态逆问题的求解,缺乏对时间序列数据信息的深入分析.提出一种基于脑部电阻抗信息的非线性动力学脑卒中诊断方法,通过EIT连续监测假手术组大鼠和右侧大脑中动脉栓塞模型组大鼠脑部,获取其阻抗变化数据,并采用递归图和递归定量分析等非线性动力学方法对监测信息的非线性特征进行提取,进而获得电阻抗信息与脑卒中病情的内在关联.实验结果表明,所提方法可为缺血性脑卒中的诊断提供依据.
Ischemic stroke poses a serious threat to health due to its high incidence and disability rates.As one method of diagnosing ischemic stroke,electrical impedance tomography(EIT)has the advantages of portability,non-invasiveness,continuous monitoring and low cost.However,existing studies on EIT mainly focus on the solution of ill-posed inverse problem for a single-frame.In-depth analysis of time series data is lacking.In this paper,based on cerebral electrical impedance information,a nonlinear kinetic method for stroke diagnosis is proposed.The cerebral impedance variation data of rats in the sham operation group and in the right middle cerebral artery embolism model group is continuously monitored and obtained by EIT.The corresponding nonlinear characteristics are extracted by the nonlinear kinetic method such as recursive graph and recursive quantitative analysis.Then inner relationship between impedance information and stroke disease is revealed.Experimental results show that the proposed method provides an alternative for the diagnosis of ischemic stroke.
作者：
施艳艳 高振 王萌 娄亚君 杨滨
Shi Yanyan;Gao Zhen;Wang Meng;Lou Yajun;Yang Bin(College of Electronic and Electrical Engineering,Henan Normal University,Xinxiang 453007,China;School of Biomedical Engineering,Air Force Medical University,Xi'an 710032,China)
机构地区：
河南师范大学电子与电气工程学院
出处：
《河南师范大学学报:自然科学版》 CAS 北大核心 2023年第6期72-76,I0003,共6页
Journal of Henan Normal University(Natural Science Edition)
基金：
国家自然科学基金(52277234) 河南省高校科技创新计划项目(21HASTIT018) 河南省自然科学基金(212300410055) 河南省高等学校青年骨干教师项目(2020GGJS061).
关键词：
缺血性脑卒中 电阻抗层析成像 非线性动力学分析 递归图 递归定量分析
ischemic stroke electrical impedance tomography nonlinear dynamic analysis recurrence plot recurrence quantification analysis
分类号：
TH772 [机械工程—精密仪器及机械]
