The Research Of ECG QRS Complex Detection Algorithm Based On Wavelet Transform

ECG(Electrocardiograph) is the reaction in the body surface of the bio-electric signals generated in the process of cardiac activity, which can objectively reflect the physiological characteristics of the heart and is an important basis to diagnose heart disease. With the emergence of dynamic electrocardiogram, huge mount ECG data makes it impossible to analyze all the data using traditional manual analysis. Automatic ECG analysis technology has become a reality from the demand and there are many kinds of automatic ECG analysis software in the market.ECG is a kind of low amplitude and low frequency bio-electric signal, and the collected ECG data usual contains a lot of interference. ECG de-noising is the premise of its waveform feature extraction, and the de-noising effect will directly affect the automatic analysis of ECG results. As the most obvious and important part in ECG, QRS complex not only contains the important cardiac physiology, but also the detection of the QRS wave is the premise of the other wave detection. On the basis of previous studies, this paper focused on the analysis of ECG de-noising and QRS complex detection technology.According to the noise sources and characteristics, the de-noising algorithm in this paper is based on wavelet transform. After selecting the appropriate wavelet basis and decomposition level, we do wavelet transform to the ECG signal. The highest-frequency coefficients and the lowest-frequency coefficients are set to zero to remove baseline drift and high frequency noise, and soft threshold method is used to other levels coefficients to weaken the EMG interference. We used the coefficients after processed to rebuild the ECG signal in which the noise is weakened or removed. We have designed a simulation experiment to validate the effectiveness of the algorithm in this paper and selected signal-to-noise ratio and minimum mean square error two parameters to evaluate the de-noising results. The result of the experiments showed that this de-noising method based on wavelet transform can effectively remove the baseline drift, the power frequency interference and EMG interference noise and maintain the authenticity of the signal.In this paper, we used wavelet transforms to detect the position of R waves in the ECG signal and quadratic spline wavelet is selected as wavelet base. This wavelet base is good at singular values detection and its simple filter coefficients makes computing speed faster. MIT-BIH arrhythmia database data verified the accuracy of the algorithm. In the paper, an improved method for the onset and offset detection of QRS complex is introduced. First, we get the envelope of the ECG signal and then construct an auxiliary signal in which the QRS complex part is strengthened and other part is weakened. Then the algorithm based on LS estimation is used to compute the onset and offset of the QRS complex. And this improved method has a good resistance of baseline drift and is applicable for variety morphology of QRS waveform. The method can be used for real-time detection because of its simple arithmetic. After the completion of the R-wave detection, QRS complex is simply classified according to the width of the QRS complex and RR interval QRS complex simple classification.The end of the article we give the holter analysis system design, and the specific function of each module, mainly focusing on data analysis, histogram analysis template editing.reporting browse.