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作 者:贾慧琳[1] 赵捷[1] 李斐[1] 张春云[1] 朱晓磊[1] 李田田[1]
机构地区:[1]山东师范大学物理与电子科学学院,山东济南250014
出 处:《现代生物医学进展》2012年第6期1160-1163,共4页Progress in Modern Biomedicine
基 金:山东省自然科学基金(ZR2010HM020);济南市科技发展计划项目(201102005)
摘 要: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 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 pre-liminary work of the T-wave alternans detection,it was evaluated on the QT Database.Results: Simulation results show that this algo-rithm 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.
分 类 号:R318[医药卫生—生物医学工程]
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