[1]刘建华,吕建峰,蔡金丹.基于卷积神经网络和长短时记忆网络的心肌梗死检测[J].中国医学物理学杂志,2022,39(11):1448-1452.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.020]
 LIU Jianhua,L?Jianfeng,CAI Jindan.Screening for myocardial infarction using convolutional neural network and long short-term memory network[J].Chinese Journal of Medical Physics,2022,39(11):1448-1452.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.020]
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基于卷积神经网络和长短时记忆网络的心肌梗死检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第11期
页码:
1448-1452
栏目:
医学人工智能
出版日期:
2022-11-25

文章信息/Info

Title:
Screening for myocardial infarction using convolutional neural network and long short-term memory network
文章编号:
1005-202X(2022)11-1448-05
作者:
刘建华吕建峰蔡金丹
三峡大学附属仁和医院心血管内科, 湖北 宜昌 443000
Author(s):
LIU Jianhua L?Jianfeng CAI Jindan
Department of Cardiovascular Medicine, Renhe Hospital Affiliated to China Three Gorges University, Yichang 443000, China
关键词:
心肌梗死心电图卷积神经网络长短时记忆网络
Keywords:
Keywords: myocardial infarction electrocardiogram convolutional neural network long short-term memory network
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.11.020
文献标志码:
A
摘要:
为了进一步提升心肌梗死的诊断效果,提出一种基于卷积神经网络和长短时记忆网络的心肌梗死检测方法,用于准确地从心电图信号中检测心肌梗死。具体来讲,提出3种分别基于卷积神经网络、卷积神经网络结合长短时记忆网络以及它们的集成模型的检测方法,以期从心电信号中检测心肌梗死和正常搏动。此外,采用数据重采样方法,即合成少数类过采样方法和Tomek Link解决数据集不平衡问题。最终与其他方法的实验结果相比,经过数据重采样的集成卷积神经网络模型的结果取得了明显优势,证明提出方法的有效性。
Abstract:
Abstract: A screening method for myocardial infarction based on convolutional neural network and long short-term memory network is proposed to accurately detect myocardial infarction from electrocardiogram (ECG) signals, thereby further improving the diagnostic efficacy of myocardial infarction. Based on convolutional neural network, convolution neural network combined with long short-term memory network, and their integration, 3 different models are put forward to detect myocardial infarction and normal beats from ECG signals. In addition, the data resampling methods, namely synthetic minority oversampling technique and Tomek Link, are used to solve the class imbalance problem of data sets. The data resampled integrated convolutional neural network has obtained better experimental results than other methods, which proves the effectiveness of the proposed method.

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备注/Memo

备注/Memo:
【收稿日期】2022-05-13 【基金项目】湖北省卫生健康科研项目(WJ2021F061) 【作者简介】刘建华,主治医师,研究方向:心血管疾病诊治,E-mail: 35802648@qq.com
更新日期/Last Update: 2022-11-25