[1]方东申,叶琪瑶,石少波,等.基于心电长时RR间期序列的心房颤动检测[J].中国医学物理学杂志,2023,40(8):1009-1015.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.014]
　FANG Dongshen,YE Qiyao,SHI Shaobo,et al.Atrial fibrillation detection based on long-term RR interval sequences of electrocardiogram[J].Chinese Journal of Medical Physics,2023,40(8):1009-1015.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.014]
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基于心电长时RR间期序列的心房颤动检测()
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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8期
页码:
1009-1015
栏目:
医学信号处理与医学仪器
出版日期:
2023-09-01

文章信息/Info

Title:
Atrial fibrillation detection based on long-term RR interval sequences of electrocardiogram
文章编号:
1005-202X（2023）08-1009-07
作者:
方东申1; 叶琪瑶1; 石少波2; 刘涛1; 李立1
1.武汉大学电子信息学院， 湖北 武汉 430072； 2.武汉大学人民医院， 湖北 武汉 430060
Author(s):
FANG Dongshen1;  YE Qiyao1;  SHI Shaobo2;  LIU Tao1;  LI Li1
1. School of Electronic Information, Wuhan University, Wuhan 430072, China 2. Renmin Hospital, Wuhan University, Wuhan 430060, China
关键词:
深度学习; 心房颤动; 心电信号; RR间期; 卷积神经网络; 长短时记忆网络
Keywords:
Keywords: deep learning atrial fibrillation electrocardiogram signal RR interval convolutional neural network long short-term memory network
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2023.08.014
文献标志码:
A
摘要:
为解决当前深度学习模型进行心房颤动检测泛化能力差的问题，提出了一种基于长时RR间期的心房颤动检测算法。基于心电信号的一维时序特性以及心房颤动的特殊RR间期，设计了CNN与LSTM结合的深度学习模型，深度挖掘长时RR序列的时间与空间特征，使得它能够在未知数据集上取得良好的结果。使用MIT-BIH心房颤动数据集的全部可用样本划分训练、验证与盲法测试集（3名个体）。通过10倍交叉验证后在盲法测试集上的准确率为99.11%、敏感性为98.86%、特异性为99.47%、阳性预测率为99.62%、F1分数为99.24%。模型与现有方法进行了对比，证实所提模型用于心房颤动检测的可行性，能够有效识别出未知数据集的心房颤动病例，泛化能力强。
Abstract:
Abstract: An atrial fibrillation detection algorithm based on long-time RR interval is proposed to solve the problem of the poor generalization ability of current deep learning models for atrial fibrillation detection. Based on the one-dimensional timing characteristics of electrocardiogram signals and the specific RR interval of atrial fibrillation, a deep learning model combining convolutional neural network and long short-term memory network is designed to deeply mine the temporal and spatial characteristics of long-term RR sequences, which enables the model to perform well on unknown data sets. All available samples of the MIT-BIH atrial fibrillation data set are divided into training, validation, and blindfold detection test sets (3 subjects). After 10-fold cross-validation, the accuracy, sensitivity, specificity, positive prediction rate, and F1 score on the blind detection test set are 99.11%, 98.86%, 99.47%, 99.62%, and 99.24%, respectively. The comparison with the existing methods confirms the feasibility of the proposed model for atrial fibrillation detection. The proposed method can effectively identify atrial fibrillation cases from unknown data sets and has a high generalization ability.

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备注/Memo

备注/Memo:
【收稿日期】2023-02-16 【基金项目】国家自然科学基金（81800447） 【作者简介】方东申，硕士研究生，研究方向：医学信号处理、深度学习，E-mail: fangdongshen@whu.edu.cn 【通信作者】李立，博士，副教授，研究方向；医学影像大数据与人工智能、医学信号检测、识别、处理与分析，E-mail: LL@whu.edu.cn
更新日期/Last Update: 2023-09-06