Design And Implementation Of R Wave Detection And Arrhythmia Classification Algorithm In The ECG Monitor

Cardiovascular disease is already one of the threats to the health of people,ECG monitoring system can detect the information of human physiological characteristics,therefore it has great significance to study the ECG monitoring system. ECG reflects the human heart activity-related information, and the study of ECG is very meaningful for the prevention, diagnosis and treatment of cardiovascular disease. This thesis study the following three aspects: pre-processing of ECG, R wave detection and arrhythmia classification.1)The pre-processing of ECG.There are three common noise in ECG--50 Hz frequency interference, low frequency baseline drift, high-frequency interference,so we design three filters to filter out them. After this,we made difference, square, and window treatments to enhanced the QRS wave characteristics.2)R wave detection. In this paper, we use a new adaptive difference threshold method to detect QRS wave, threshold value in the process of testing can be updated in real time.To exclude the interference of noise signal,we set up two threshold-peak threshold and noise threshold. At the same time we set up two levels of detection to obtain R peak value, such as a rough orientation and accurate positioning, which can increase the rate of R wave detection. At last, by testing the R wave location and width of the information we calculate the heart rate value.3)Arrhythmia classification. For common arrhythmia category cardiac arrest, leakage, bradycardia, tachycardia and RONT arrhythmia, we use the method of characteristic parameters in combination with logic branch judgment to classy them; for vent, Ventricular bigeminy law, Ventricular triple law,we used multi-template matching algorithms to classify.ECG monitoring can realize real-time monitoring ECG of human, measure heart rate, output type arrhythmia, statistics of ventricular premature beat(PVC) number.