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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。

英文摘要:

Objective: To realize the detection of T-wave interval with the Wavelet Transform. Methods: R-peak was located by using modulus maxima algorithm on the 23 scale. According to the R-peak as well as the empirical values of T-wave beginning and T-wave end, the temporary T-wave intervals were identified on the 24 scale. The T-wave modulus maxima pairs of every beat were found in those temporary T-wave intervals. And then the T-wave morphology was determined on the basis of modulus maxima pairs' quantity and plus-minus. As different T-wave morphologies correspond with different detection methods of the T-wave beginning and T-wave end, classification detection of T-wave interval was used to improve the detection accuracy. Because this algorithm is regarded as the preliminary work of the T-wave alternans detection, it was evaluated on the QT Database. Results: Simulation results show that this algorithm can successfully distinguish the morphology of each T-wave. And T-wave interval detection can be more accurate based on this method. Conclusion: In this article, the modulus maxima algorithm is used to detect the T-wave interval, considering the different T-wave morphologies. Using this algorithm can make simulation results reach our expectation, meanwhile, easy to calculate and to realize.

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