【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。更多还原

【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.更多还原