[1]冀常鹏,邓伟,代巍.基于马尔可夫转移场与改进MobileNetV2的心律失常分类方法[J].中国医学物理学杂志,2023,40(11):1395-1401.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.013]
　JI Changpeng,DENG Wei,DAI Wei.Markov transfer field combined with modified MobileNetV2 for arrhythmia classification[J].Chinese Journal of Medical Physics,2023,40(11):1395-1401.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.013]
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基于马尔可夫转移场与改进MobileNetV2的心律失常分类方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第11期
页码:
1395-1401
栏目:
医学信号处理与医学仪器
出版日期:
2023-11-24

文章信息/Info

Title:
Markov transfer field combined with modified MobileNetV2 for arrhythmia classification
文章编号:
1005-202X（2023）11-1395-07
作者:
冀常鹏; 邓伟; 代巍
辽宁工程技术大学电子与信息工程学院， 辽宁 葫芦岛 125105
Author(s):
JI Changpeng;  DENG Wei;  DAI Wei
School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
关键词:
心律失常; 马尔可夫转移场; MobileNetV2; 二维图像; 注意力机制
Keywords:
cardiac arrhythmia Markov transfer field MobileNetV2 two-dimensional image attention mechanism
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.11.013
文献标志码:
A
摘要:
心律失常自动分类对心血管疾病的预防尤为重要，本研究提出一种基于马尔可夫转移场（MTF）和改进MobileNetV2网络的心律失常图像分类方法。首先将原始心电（ECG）信号进行预处理和数据增强，并通过MTF将处理后的ECG片段转变为具有时间关联性的二维图像。其次在MobileNetV2网络的模块中融入高效通道注意力。将正常心拍、左束支传导阻滞、右束支传导阻滞和起搏心拍4种类型的ECG信号通过改进MobileNetV2网络进行分类。结果表明改进MobileNetV2模型复杂度仅略高于原始MobileNetV2，在心律失常分类准确率上，比原始MobileNetV2网络提高0.89%，达到99.71%，实现了对4种不同类型的ECG信号的有效分类。
Abstract:
The automatic arrhythmia classification is critical for cardiovascular disease prevention. An approach for arrhythmia classification based on Markov transfer field (MTF) and modified MobileNetV2 network is presented. After preprocessing and data enhancement for the original electrocardiogram (ECG) signals, MTF maps the processed ECG segments into two-dimensional images with temporal correlation, and then a modified MobileNetV2 network which incorporates with efficient channel attention classifies the ECG signals of 4 types: normal beat, left bundle-branch block, right bundle-branch block, and paced beat. The results show that the modified MobileNetV2 is slightly more complex than the original MobileNetV2, and it has a classification accuracy of 99.71%, which is 0.89% higher than the original MobileNetV2, demonstrating that the proposed approach can achieve the effective arrhythmia classification.

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备注/Memo

备注/Memo:
【收稿日期】2023-06-10 【基金项目】辽宁省教育厅基本科研项目（LJKMZ20220677） 【作者简介】冀常鹏，教授，研究方向：计算机通信与网络、工程机械控制、微弱信号处理与辨识，E-mail: ccp@lntu.edu.cn
更新日期/Last Update: 2023-11-24