[1]王凯,杨枢,李超. 一种基于ECG的多层共轭对称Hadamard特征变换的房颤异常信号分类方法[J].中国医学物理学杂志,2019,36(9):1068-1073.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.014]
　WANG Kai,YANG Shu,LI Chao. ECG-based multi-level conjugate symmetric Hadamard feature transformation for classification of abnormal signals of atrial fibrillation[J].Chinese Journal of Medical Physics,2019,36(9):1068-1073.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.014]
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 一种基于ECG的多层共轭对称Hadamard特征变换的房颤异常信号分类方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第9期
页码:
1068-1073
栏目:
医学信号处理与医学仪器
出版日期:
2019-09-25

文章信息/Info

Title:
 ECG-based multi-level conjugate symmetric Hadamard feature transformation for classification of abnormal signals of atrial fibrillation
文章编号:
1005-202X（2019）09-1068-06
作者:
 王凯; 杨枢; 李超
 蚌埠医学院卫生管理学院， 安徽 蚌埠 233030
Author(s):
 WANG Kai;  YANG Shu;  LI Chao
 Department of Health Management, Bengbu Medical College, Bengbu 233030, China
关键词:
 ; 房颤; 心电图; 多层共轭对称Hadamard特征变换; Levenberg-Marquardt神经网络
Keywords:
 ; Keywords: atrial fibrillation;  electrocardiogram;  multi-level conjugate symmetric Hadamard feature transformation;  Levenberg-Marquardt neural network
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.09.014
文献标志码:
A
摘要:
 目的：对心电图房颤异常信号进行检测和分析，利用多层共轭对称Hadamard特征变换模型，构建房颤异常信号分类系统。方法：采用多层共轭对称Hadamard特征变换的房颤识别方法，检测房颤异常信号分类特征。采用基于误差梯度反向传播Levenberg-Marquardt神经网络模型训练测试数据集。构建房颤异常信号分类器，并建立临床诊断分类模型。结果：该模型能有效提高特征分类效果，增加算法的收敛速度及计算精度，便于实时分析和诊断房颤异常疾病。结论：该模型能够捕获异常房颤信号的疑似波形，评估和分析信号特征，具有较高的系统鲁棒性。
Abstract:
 Abstract: Objective To detect and analyze the abnormal signals of atrial fibrillation (AF) by multi-level conjugate symmetric Hadamard feature transformation model for establishing a system to classify AF abnormal signals. Methods The features for the classification of AF abnormal signals were detected with multi-level conjugate symmetric Hadamard feature transformation. Levenberg-Marquardt neural network model based on error gradient back-propagation was used for the training of test data set. A classifier for the classification of AF abnormal signals was constructed, and finally a classification model used in clinical diagnosis was established. Results The proposed model effectively improved the performance of feature classification, increased convergence speed and algorithm accuracy, thereby facilitating the real-time analysis and diagnosis of AF. Conclusion The proposed model which has high system robustness can be used to capture suspected waveforms of AF abnormal signals, evaluate and analyze signal features.

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备注/Memo

备注/Memo:
 【收稿日期】2019-03-15
【基金项目】蚌埠医学院科技发展基金（BYKF1717）
【作者简介】王凯，硕士，讲师，研究方向：心电信号处理、模糊识别，E-mail: wangkai0552@126.com
【通信作者】杨枢，硕士，教授，研究方向： 数据挖掘，E-mail: yangshu05-
52@bbm.edu.cn
更新日期/Last Update: 2019-09-23