基于自组织神经网络的QRS波聚类方法 |
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引用本文: | 孙括,王云峰,徐静波,张海英.基于自组织神经网络的QRS波聚类方法[J].传感器与微系统,2017,36(2). |
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作者姓名: | 孙括 王云峰 徐静波 张海英 |
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作者单位: | 1. 中国科学院大学电子电气与通信工程学院,北京101407;中国科学院微电子研究所新一代通信射频芯片技术北京市重点实验室,北京100029;2. 中国科学院微电子研究所新一代通信射频芯片技术北京市重点实验室,北京,100029 |
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摘 要: | 为提高查看大量数据动态心电(ECG)图时的效率,将波形聚类,采用埃尔米特函数和自组织神经网络,实现了室性早搏占比高情况下的心电波形聚类算法.使用MIT-BIH心率失常数据库,利用埃尔米特函数分解QRS波形为QRS向量,将所有QRS向量输入自组织神经网络进行分类.使用特征向量元素分析聚类结果,用阳性率指标对结果进行统计,平均真阳性率为91.2%,假阳性率为1.03%,验证了基于自组织神经网络的心电聚类算法的有效性.达到了将正常心搏和室性早搏心搏聚类的目标.
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关 键 词: | QRS 聚类 埃尔米特函数 自组织神经网络 阳性率 |
Method for QRS wave clustering using self-organizing neural network |
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Abstract: | To improve the efficiency when checking the morphology of the large dataset of a dynamic ECG and cluster QRS complexes,a method for QRS clustering is realized using Hermite functions and self-organizing neural network with premature ventricular contraction in high proportion.Using MIT-BIH arrhythmia database,a QRS complex is decomposed into a QRS vector by Hermit functions.All the vectors are input into self-organizing neural network for clustering.Elements of the vectors are used to analyze the clustering result.Positive rate is used to give a statistics result.Method achieves an average true positive rate of 91.2 % and an average false positive rate of 1.03 %.The method is effective in clustering using self-organizing neural network.The aim of clustering normal beats and premature ventricular contraction beats is attained. |
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Keywords: | QRS clustering Hermite functions self-organizing neural network positive rate |
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