Design And Implementation Of Detecting Characteristic Data Algorithm In ECG Signal

In recent years, as one of the major diseases, cardiovascular disease has become a threat to human health. ECG analysis is a hot research area because it can reflect the state of the heart fluctuations intuitively. This thesis is about the study and implementation of the algorithm in detecting characteristic points of ECG signals. Opposite to the hardware detection, it is the method of software in ECG data detection. QRS waves contain important information about the heart condition and it is the prerequisite of detecting the other ECG data such as the P wave and T wave, so accurately identifying the QRS waves make the important significance for heart disease prevention and diagnosis.The algorithms are based on the application of a Micro ECG Device for the two data processing patterns, one is single instrument detection and the other is detection with PC connection. The two different environments have different computing and storage capacity, so the differential threshold method and wavelet transform method are adapted to the detection and location of QRS waves in two patterns.In the implementation of the differential threshold method, based on real-time data processing, designed short-range time as a unit of data block mode, to achieve continuously updated the characteristic points. Using the half width of R wave detection method to eliminate the mistake of the R wave detection, the method can improve the R wave detection accuracy.In the implementation of the wavelet transform method, using the character in multi-scale analysis of wavelet, designed the Mallat algorithm based on dyadic Marr wavelet transform to detect the QRS waves. Using an integrative method to identify the result, it can detect the QRS waves in the specific scale with effective inhibition of high-frequency noise, such as power-line interference, the method can improve the R wave detection accuracy. The P wave and T wave detection ideas and methods are proposed.This thesis introduced the widely used MIT-BIH ECG data as the object of processing algorithms and standard to measure the accuracy of the algorithm. Making objective scientific evaluation with the algorithm by checking result compared with the standard annotation of MIT-BIH. Test result shows that the two designed algorithms can meet the practical applications with high accuracy.