【作者】 孙静；

【导师】 黄丹飞；

【作者基本信息】 长春理工大学 ， 生物医学工程， 2014， 硕士

【摘要】 脑梗塞是最常见的脑血管疾病。当患有脑梗塞疾病时,患者的心电信号和脑电信号等多种生理信号会有相应的异常变化,而临床中的监护仪器不能对脑梗塞引发的异常多生理信号作出关联分析。因此,医护人员不能凭借监护仪器快速的对脑梗塞疾病进行诊断。本课题提出一种多生理信号关联分析的方法,为医护人员快速诊断脑梗塞疾病提供依据。本论文采用心电信号模块,脑电信号模块,脑阻抗信号模块采集大鼠脑梗塞时的心电信号,脑电信号和脑阻抗信号,利用数字滤波器和小波分析的方法对多生理信号滤波处理,并结合差分阈值法和极大值法提取多生理信号的特征参数。应用数据挖掘中的关联规则Apriori算法对大鼠脑梗塞时多生理信号的特征参数进行关联分析。结果表明,大鼠脑梗塞时引发的多生理信号特征参数之间确实存在一定的关联关系,从而可以通过多生理信号的关联分析来预测脑梗塞疾病,为临床中诊断脑梗塞疾病提供了一种新的疾病诊断依据。更多还原

【Abstract】 Cerebral infarction disease is the most common cerebral vascular disease. When suffering from cerebral infarction disease, ECG, EEG and other physiological signals of patients have abnormal changes. However, medical monitoring instrument cannot make related analysis of these physiological signals. Therefore, the medical staff cannot quickly diagnose cerebral infarction disease by monitoring instrument. This paper presents a method of analysis of physiological signal correlation, in ordering to provide the basis for the rapid diagnosis of cerebral infarction disease.Collect physiological signals of rats with cerebral infarction by ECG module, EEG module and brain impedance module. Use digital filter and wavelet analysis to process physiological signals. Use differential threshold method and the maximum value method to extract the characteristic parameters of physiological signals. Make correlation analysis of physiological signals of rats with cerebral infarction using Apriori algorithm. The result show that there are indeed some correlation between physiological signal feature parameters, which we can use to predict cerebral infarction disease and provides a new basis for the rapid clinical diagnosis of cerebral infarction disease.更多还原