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QRS complex detection based on multi wavelet packet decomposition

机译:基于多小波包分解的QRS复杂检测

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摘要

We present in this paper a wavelet packet based QRS complex detection algorithm. Our proposed algorithm consists of a particular combination of two vectors obtained by applying a designed routine of QRS detection process using 'haar' and 'db10' wavelet functions respectively. The QRS complex detection routine is based on the histogram approach where our key idea was to search for the node with highest number of histogram coefficients, at center, which we assume that they are related to the iso-electric baseline whereas the remaining least number coefficients reflect the R waves peaks. Following a classical approach based of a calculated fixed threshold, the possible QRS complexes will be determined. The QRS detection complex algorithm has been applied to the whole MIT-BIH arrhythmia Database to assess its robustness. The algorithm reported a global sensitivity of 98.68%, positive predictive value of 97.24% and a percentage error of 04.12%. Eventhough, the obtained global results are not as excellent as expected, we have demonstrate that our designed QRS detection algorithm performs good on a partial selected high percentage of the whole database, e.g., the partial results, obtained when applying the algorithm on 85.01% of the whole MIT-BIH arrhythmia Database, are 99.14% of sensitivity, 98.94% of positive predictive value and 01.92% of percentage error.
机译:我们在本文中提出了一种基于小波包的QRS复杂检测算法。我们提出的算法由两个向量的特定组合组成,这两个向量是通过分别使用“ haar”和“ db10”小波函数应用QRS检测过程的设计例程而获得的。 QRS复杂检测例程基于直方图方法,其中我们的主要思想是在中心搜索直方图系数最大的节点,我们假设它们与等电基线相关,而其余的系数最小反映出R波的峰值。遵循基于计算的固定阈值的经典方法,将确定可能的QRS络合物。 QRS检测复杂算法已应用于整个MIT-BIH心律失常数据库,以评估其鲁棒性。该算法报告的整体敏感性为98.68%,阳性预测值为97.24%,百分比误差为04.12%。尽管获得的全局结果不如预期的那么好,但我们已经证明,我们设计的QRS检测算法在整个数据库的部分选定的较高百分比上表现良好,例如,将算法应用于85.01%的部分结果整个MIT-BIH心律失常数据库的敏感性为99.14%,阳性预测值为98.94%,误差百分比为01.92%。

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