[1]蔚文婧,王寻,张鹏远,等.一种基于多层感知器的房颤心电图检测方法[J].中国医学物理学杂志,2020,37(3):332-336.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.015]
　WEI Wenjing,WANG Xun,ZHANG Pengyuan,et al.Multilayer perceptron-based method for atrial fibrillation ECG detection[J].Chinese Journal of Medical Physics,2020,37(3):332-336.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.015]
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一种基于多层感知器的房颤心电图检测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第3期
页码:
332-336
栏目:
医学信号处理与医学仪器
出版日期:
2020-03-25

文章信息/Info

Title:
Multilayer perceptron-based method for atrial fibrillation ECG detection
文章编号:
1005-202X（2020）03-0332-05
作者:
蔚文婧1; 王寻1; 张鹏远1; 颜永红1; 2; 3
1.中国科学院声学研究所语言声学与内容理解重点实验室， 北京 100190； 2.中国科学院新疆理化技术研究所新疆民族语音语言信息处理实验室， 新疆 乌鲁木齐 830011； 3.中国科学院大学， 北京 100049
Author(s):
WEI Wenjing1;  WANG Xun1;  ZHANG Pengyuan1;  YAN Yonghong1;  2;  3
1. Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China; 2. Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
关键词:
房颤; 心电图; 多层感知器; R波检测; 深层置信网络
Keywords:
Keywords: atrial fibrillation;  electrocardiogram;  multilayer perceptron;  R-wave detection;  deep belief network
分类号:
R318；TP183
DOI:
DOI:10.3969/j.issn.1005-202X.2020.03.015
文献标志码:
A
摘要:
目的：提出一种基于多层感知器（MLP）的新型房颤识别算法。方法：首先设计一种新型自适应的R波阈值检测算法，然后以R波位置和幅度为特征，MLP为分类器进行正常/房颤心电图识别。MLP的网络参数采用深层置信网络预训练算法进行初始化，最后用误差反向传播算法对MLP网络权重进行调整。结果：在单通道心电图数据集上对正常、房颤心电信号进行分类，本研究方法的灵敏度达96.00%，特异性为84.18%，平均识别率为90.09%。结论：这种基于MLP的心电识别算法准确率高、计算复杂度较低，可为房颤的智能诊断提供一种新方法。
Abstract:
Abstract: Objective To propose an atrial fibrillation (AF) recognition method based on multilayer perceptron (MLP). Methods Firstly, a novel R-wave detection algorithm based on adaptive threshold was designed, and then with the location and amplitude of R-wave as features, MLP was used as classifier to recognize the normal/AF electrocardiogram (ECG). The network parameters of MLP were initialized by deep belief network pre-training algorithm. Finally, the weights of MLP network were tuned by error back-propagation (BP) algorithm. Results The sensitivity, specificity and average recognition rate of the proposed method for the classification of normal and AF ECG signals on a single-channel ECG database were 96.00%, 84.18% and 90.09%, respectively. Conclusion The proposed algorithm based on MLP which has high accuracy and lower computation complexity can be a new method for the intelligent diagnosis of AF.

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备注/Memo

备注/Memo:
【收稿日期】2019-10-03 【基金项目】国家自然科学基金（11590770-4, 31600868, 11774380） 【作者简介】蔚文婧，博士后，研究方向：医学信号智能诊断，E-mail: weiwenjing@hccl.ioa.ac.cn 【通信作者】张鹏远，博士，研究员，研究方向：语音识别、人工智能，E-mail: zhangpengyuan@hccl.ioa.ac.cn
更新日期/Last Update: 2020-04-02