基于小波的消噪成像与微心电数据特征检测的研究

The Integrated Application of Wavelet Theory in ECG Denoising and Detection of QRS Wave

本文首先采用小波分解重构法有效滤除基线漂移分量,其次使用小波阈值法来滤除工频干扰等噪声,最后采用了一种基于双正交二次B样条小波的检测算法,利用小波变换与信号奇异点的关系,在23尺度下识别R波,在21尺度下对QRS波的起点和终点进行检测。利用美国MIT-BIH心电数据库对算法进行的分析表明:算法在确保较小失真度的同时能很好滤除基线漂移、工频等多种噪声干扰,并能保证99.67%以上的检测率。

First,adopts the decomposition and reconstruction algorithm to restrict the baseline wander by use decomposition and reconstruction algorithm.Second,filter the power-line interference using wavelet thresholdingthresholding.Last,we adopt a QRS wave detection arithmetic based on 2-order B-Spline wavelet transforms.Using the relationship between wavelet transforms and signal singularity point,we detect the peak of R wave in scale 23,the begin and the end of QRS wave in scale 21.we tested the algorithm though MIT-BIH datebase,the final results confirm that the algorithm effectively eliminates noises while gives little distortion,and also has a high detective veracity,upwards of 99.67%.