Study On ECG Signal Processing And HRV Analysis

The electrocardiogram(ECG) technique has been one of indispensable methods for diagnosis,observation and research of cardiology,because of its good characteristics.For example,easily operation,noninvasive,etc.In decades of years, the clinical applied area of ECG has become wider and wider.Some techniques use it for signing the time and phase.In the 863 project "Technique and Device for Detecting the Function and Status of Cardiovascular System" the ECG signal is used to mark phonocardiogram signal and pulse wave signal and evaluate the function and status of cardiovascular system.Because of the mixed other biomedical signal and electromagnetic interference the ECG signal has more noise and is harder to measure. Acording to this characteristic,we remove the wander of the baseline and detect R wave base on the theory of wavelet transform.The result of the experiment is very good.Heart Rate Variability(HRV)based on RR interval is a nontraumatic diagnosis method developed in the recent 20 years for assessing the function of antonomic nervous system of heart,which has found application in scientific research and clinic.With accurate R wave position,analysis of HRV based on RR interval have been studied,including time domain measures,frequency domain measures and time—frequency domain analyses and nonlinear methods have been introduced.Current problems and trend of development have been predicted.In Chapter Two the ECG preprocessing and characteristic point detection has been studied,including the elimination of high frequency noise,baseline wander and power line interference.The principle to detect odd signals by wavelet is showed,in which the position of odd point in a signal is determined by corresponding relation between those extremums of its wavelet transform and odd points.When it comes to the detection of those abnormal ECG signal,a new algorithm was proposed to detect inverse R wave.The results were very good.Based on accurate R wave detection,we overviewed the indicator and evaluation methods for HRV,and calculated the indicator of SDNN,SDANN,PNN50 and so on of two patiens,one is almost healthy,the other is ST-T changed,by use of the database of MIT,which is the main content of chapter Three.And the relults was compared.And chapter Four shows several new methods to analyzing the power spectral density,which comes from frequency analysis,and indicates the research trend of this technique.Based on multi-resolution analysis nd wavelet packet analysis,chapter Five reveals power density of each frequency bands of 5 minutes signal from two groups by wavelet packet transform and analysis age factor influence by calculating low frequency/high frequency power ratio with the help of WPT.