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目的:利用小波变换进行T波区间的检测。方法:在23尺度上通过模极大值法定位R波。在24尺度上首先根据R峰以及T波起点和终点的经验值确定起始T波区间。然后对每个心拍在此区间上找到T波的模极大值,根据模极值的个数和正负顺序确定T波波形的形态。由于不同形态的T波对应不同的T波起点和终点的检测方法,实现T波区间的分类检测,提高T波检测的精确度。由于本文算法是作为T波交替检测的前期工作,为了验证算法的准确率,采用了QT数据库中的部分记录进行了仿真,评价实验结果。结果:仿真实验证明了本文算法能正确地分辨出每个T波的形态,并在此基础上得到较为准确的T波区间。结论:本文采用模极大值算法根据T波的不同形态进行T波区间的分类检测,检测结果比较理想,且计算简单,较易实现。
Objective: To detect the T wave interval using wavelet transform. Methods: The R-wave was located by the mode maximum at 23 scales. The initial T-wave interval is first determined on the 24 scale based on the R-peak and the empirical values of the start and end of the T-wave. Then for each heart beat in this interval to find the maximum modulus of T wave, according to the modulus of the number of positive and negative sequence to determine the shape of T wave. Because different forms of T waves correspond to different detection methods of the start and end points of the T wave, classification detection of the T wave interval can be realized and the accuracy of the T wave detection can be improved. Because the algorithm of this paper is used as the preliminary work of T wave alternation detection, in order to verify the accuracy of the algorithm, part of the records in the QT database are used to simulate and evaluate the experimental results. Results: Simulation results show that this algorithm can correctly distinguish the shape of each T-wave, and on this basis, get a more accurate T-wave interval. Conclusion: In this paper, the modulo maximum algorithm is used to classify the T-wave interval according to the different forms of T-wave. The detection results are ideal, and the calculation is simple and easy to implement.