[1]吴义满.一种基于经验小波变换的心电信号室性早搏检测算法[J].中国医学物理学杂志,2018,35(9):1063-1068.[doi:DOI:10.3969/j.issn.1005-202X.2018.09.013]
　WU Yiman.Detecting premature ventricular contractions in ECG signals with empirical wavelet transformbased algorithm[J].Chinese Journal of Medical Physics,2018,35(9):1063-1068.[doi:DOI:10.3969/j.issn.1005-202X.2018.09.013]
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一种基于经验小波变换的心电信号室性早搏检测算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第9期
页码:
1063-1068
栏目:
医学信号处理与医学仪器
出版日期:
2018-09-27

文章信息/Info

Title:
Detecting premature ventricular contractions in ECG signals with empirical wavelet transformbased algorithm
文章编号:
1005-202X（2018）09-1063-06
作者:
吴义满
江苏医药职业学院医学影像学院，江苏盐城224000
Author(s):
WU Yiman
School of Medical Imaging, Jangsu Vocational College of Medicine, Yancheng 224000, China
关键词:
心电信号; 神经网络; 室性早搏; 经验小波变换; 特征提取
Keywords:
Keywords: electrocardiogram signal;  neural network;  premature ventricular contraction;  empirical wavelet transform;  characteristic extraction
分类号:
R331.38；R318
DOI:
DOI:10.3969/j.issn.1005-202X.2018.09.013
文献标志码:
A
摘要:
针对心电信号中的室性早搏心拍检测问题，使用经验小波变换（EWT）实现心电信号的自适应分解。根据心电信号时频能量变化特征，提出了一种低复杂度的频域累积能量特征计算方法，并分析了室性早搏与正常心电信号的特征差异性。最后利用反向传播神经网络在MIT-BIH心电数据库上进行心拍样本训练与识别测试。结果表明基于EWT的特征提取避免了传统时域特征提取中的QRS波群检测过程，降低了其它干扰因素对诊断结果的影响，具有较高的分类精度与良好的鲁棒性，总体敏感度与总体阳性检测率分别达到96.55%和97.73%。
Abstract:
Abstract: In view of the problems in the detection of premature ventricular contractions in electrocardiogram (ECG) signals, empirical wavelet transform is proposed to achieve the adaptive decomposition of ECG signals. According to the time-frequency energy variation characteristics of ECG signals, a low-complexity method is propose to obtain the characteristics of frequencydomain cumulative energy, and the characteristic differences between premature ventricular contractions and normal ECG signals are analyzed. Finally, back-propagation neural network is used to perform training and detection test on MIT-BIH ECG database. The results show that the characteristic extraction based on empirical wavelet transform avoids the detection process of QRS complexes in the traditional time-domain characteristic extraction and reduces the effects of other interference factors on the diagnosis results, with a high classification accuracy and good robustness. The overall sensitivity and positive detection rate of the proposed method reach 96.55% and 97.73%, respectively.

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备注/Memo

备注/Memo:
【收稿日期】2018-06-20
【基金项目】江苏省科技厅自然科学基金项目（BK20151293），该项目为江苏省品牌示范高职院校重点建设项目
【作者简介】吴义满，讲师，硕士研究生，研究方向：生命信号处理，E-mail: wamzqy0661@sina.com
更新日期/Last Update: 2018-09-29