Analysis Of Cerebral Infarction Disease Base On Data Mining

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.