[1]王量弘,蔡冰洁,刘硕,等.基于卷积神经网络与通道和空间注意力机制的房颤预测模型研究[J].福建医药杂志,2024,46(01):1-4.[doi:10.20148/j.fmj.2024.01.001]
　WANG Lianghong,CAI Bingjie,LIU Shuo,et al.Study on atrial fibrillation prediction model based on convolutional neural network and CBAM attention mechanism[J].FUJIAN MEDICAL JOURNAL,2024,46(01):1-4.[doi:10.20148/j.fmj.2024.01.001]
点击复制

基于卷积神经网络与通道和空间注意力机制的房颤预测模型研究()
分享到：

《福建医药杂志》[ISSN:1002-2600/CN:35-1071/R]

卷:
46
期数:
2024年01期
页码:
1-4
栏目:
论著
出版日期:
2024-02-15

文章信息/Info

Title:
Study on atrial fibrillation prediction model based on convolutional neural network and CBAM attention mechanism
文章编号:
1002-2600（2024）01-0001-04
作者:
王量弘1; 蔡冰洁1; 刘硕1; 杨涛1; 王新康2; 高洁2
1 福州大学物理与信息工程学院，福州 350108；2 福建省立医院心电诊断科，福州 350001
Author(s):
WANG Lianghong1;  CAI Bingjie1;  LIU Shuo1;  YANG Tao1;  WANG Xinkang2;  GAO Jie2
1 College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; 2 ECG Diagnosis Department of Fujian Provincial Hospital, Fuzhou, Fujian 350001, China
关键词:
心电信号; 房颤; 卷积神经网络; 通道和空间注意力机制
Keywords:
ECG signals;  atrial fibrillation;  convolutional neural networks;  convolutional block attention module
分类号:
R541.7+5
DOI:
10.20148/j.fmj.2024.01.001
文献标志码:
A
摘要:
目的采用人工智能技术提出一种模型，以对房颤进行早期预防和诊断。方法提出一种基于卷积神经网络（convolutional neural network, CNN）与通道和空间注意力机制（convolutional block attention module，CBAM）的模型用于对房颤的诊断与预测。结果根据长期心房颤动数据库、MIT-BIH心房颤动数据库和MIT-BIH正常窦性心律数据库的数据, 提出的模型在全盲的情况下总体准确率达94.2%。结论提出的模型满足了医学心电图解释的需要，为房颤的预测研究提供了新思路。
Abstract:
ObjectiveTo propose a model for early prevention and diagnosis of atrial fibrillation by using artificial intelligence technology.MethodsA model based on convolutional neural network(CNN) and convolutional block attention module(CBAM) was proposed for the diagnosis and prediction of atrial fibrillation.ResultsThe overall accuracy of the proposed model reached 94.2% in the case of total blindness based on the data from the long-term atrial fibrillation database, the MIT-BIH atrial fibrillation database and the MIT-BIH normal sinus rhythm database.ConclusionThe proposed method satisfies the needs of medical ECG interpretation and provides a new idea for the prediction of atrial fibrillation.

参考文献/References:

[1] 何康,武忠.人工智能在心房颤动预测中的新进展[J].中国胸心血管外科临床杂志,2020,27(12):1472-1478.
[2] GLADDING P,LEGGET M,FATKIN D,et al.Polygenic risk scores in coronary artery disease and atrial fibrillation[J].Heart,Lung and Circulation,2020,29(4):634-640.
[3] HU W S,HSIEH M H,LIN C L.A novel atrial fibrillation prediction model for Chinese subjects：a nationwide cohort investigation of 682237 study participants with random forest model[J].Europace,2019,21(9):1307-1312.
[4] MOHEBBI M,GHASSEMIAN H.Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability[J].Medical &Biological Engineering & Computing,2014,52(5):415-427.
[5] ALCARAZ R,RIETA J J.Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation[J].Medical Engineering & Physics,2009,31(8):917-922.
[6] SUN L,WANG Y,HE J,et al.A stacked LSTM for atrial fibrillation prediction based on multivariate ECGs[J].Health Information Science and Systems,2020,8(1)：1-7.
[7] NGUYEN Q H,NGUYEN B P,NGUYEN T B,et al.Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings[J].Biomedical Signal Processing and Control,2021,68:102672.
[8] ATTIA Z I,NOSEWORTHY P A,LOPEZ-JIMENEZ F,et al.An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm：a retrospective analysis of outcome prediction[J].The Lancet,2019,394(10201)：861-867.
[9] PETRUTIU S,SAHAKIAN A V,SWIRYN S.Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans[J].Europace,2007,9(7):466-470.
[10] MOODY G B,MARK R R.A new method for detecting atrial fibrillation using R-R intervals[J].Computers in Cardiology,1983,10:227-230.
[11] GOLDBERGERA L,AMARAL L A,GLASS L,et al.PhysioBank,PhysioToolkit,and PhysioNet：components of a new research resource for complex physiologic signals[J].Circulation,2000,101(23)：e215-e220.
[12] WOO S,PARK J,LEE J Y,et al.CBAM：Convolutional Block Attention Module.in：15th European conference on Computer Vision(ECCV)[C].BERLIN:SPRINGER-VERLAG BERLIN,2018：3-19.

备注/Memo

备注/Memo:
基金项目：国家自然科学基金面上项目（61971140）
更新日期/Last Update: 2024-02-15