Design And Implementation Of Embeded ECG Detection And Analysis System

With heart disease has become one of the major disease threat to human health,intelligent ECG analysis system has been developing rapidly in the field of electronics.ECG contains a large number of cardiac status information,it is an important method of clinical diagnosis of heart disease.For real-time monitoring of heart health,develope a small portable ECG detection analysis system is necessary and full of high value.The aging population,urban life stress and disease prevention are three main internal driving forces which make family ECG analysis system develop rapidly.At the same time,the rapid development of electronic technology constitute the external power.Under the internal and external impetus function,ECG intelligent analyzer market momentum rapidly.A variety of intelligent ECG analysis method are studied in this thesis.The application of wavelet transform in ECG processing shows good results,including preprocessing and feature extraction.R wave detection of ECG signal is carried out by wavelet transform method,the detection rate of R wave has reached more than 98%.Then according to the shortage of traditional feature extraction method,the author uses continuous wavelet transform algorithm to solve the problem which could easily describe P and QRS complex in shape and details.The simulating results show that the continuous wavelet transformation method not only apply less feature vectors to implement complete and fast classification process but also overcome the former method in accuracy.This thesis apply BP network into ECG intelligent classification.There are 5 kinds of labels and the classification accuracy is over 95%.At the same time,this thesis also apply the method of statistical characteristics into ECG classification.And this method can analysis 5 other labels.These two method can use as an entirety so that we can get ten kinds of intelligent analysis ECG We can also easily locate the onset time.This is one of the highlights of this thesis.ECG intelligent analysis contains four main modules:ECG detection,preprocessing,feature extraction and classification.This thesis adopted the authorityMIT-BIH database.The processor platform is ARM,operating system is Linux,and use Qt as graphical interface development platform.ECG processing and feature extraction is based on wavelet transform,this thesis use two methods to realize the function of ECG signals intelligence analysis,one is BP neural network and the other one is the based-on statistical characteristics.This thesis introduces the realization of the function of these methods and processes in detail.