Wavelet Transform And Fuzzy Neural Network Ecg Automatic Analysis

Cardiovascular diseases are one of the main diseases that endanger human's health nowadays. The electrocardiogram (ECG) examination is very important for clinical diagnosis of cardiovascular diseases. The application of computers at the accurate and automatic analysis of ECG has been a hotspot to researchers at home and overseas. The missions of the automatic analysis include the filter pretreatment of ECG signal and the examination of ECG characteristics etc. The final purpose is to classify ECG and diagnose diseases according to the characteristics of ECG. In various cardiovascular diseases, the Premature Ventricular Contract (PVC) is one of the most familiar diseases of the arrhythmia. Its real-time and accurate examination is an important technique of ECG automatic analysis. It operates a key function in increasing the performance of arrhythmia custody system and Holter ECG analysis system. It also has important practical value in improving the heart disease diagnosis.There have been had a lot of reports on the use of wavelet transform in carrying on the filter pretreatment of ECG signal and examining the ECG characteristics, and the use of fuzzy reasoning and artificial neutral network in classifying and identifying ECG. But these techniques aren't perfect. In this paper, the applications of wavelet transform and fuzzy neural network at ECG automatic analysis were further researched and discussed. We mainly did following several works on the base of summarizing the past research work.Firstly, a modified method of ECG filter based on wavelet transform was put forward. That is to combine the wavelet transformand adaptive filter. It is an innovation of this paper. The experimental results demonstrated that the noises of ECG signal were successfully removed, the useful ECG information was perfectly preserved, and the modified denoising method was efficient and had better capability of filtering.Secondly, the ECG examination was further researched and discussed using the signal's singularity examination theory of wavelet transform. Namely, Marr wavelet filters were designed, analyzed the discrete binary wavelet transform of ECG signal from the point of equivalent filter, the R wave, jumping-off point and end point of QRS wave, P wave and T wave were accurately examined by using Marr wavelet transform at ECG signal and examining the maximum line of wavelet transform. In the examination of R wave, several strategies such as volatile thresholds, disapprobatory interval, and Lipschitz exponent so on, were used for increasing the correctness of ECG characteristic examination.Thirdly, the automatic examination of PVC was researched. The automatic identification of normal ECG and PVC were realized using the classification and identification abilities of artificial neutral network with fuzzy input and output. The fuzzy neural network was proved having better automatic identification ability, after training and testing the neural network using the ECG datum of MIT-BIH database.