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[1]王量弘,蔡冰潔,劉碩,等.基于卷積神經(jīng)網(wǎng)絡(luò)與通道和空間注意力機制的房顫預(yù)測模型研究[J].福建醫(yī)藥雜志,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]
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基于卷積神經(jīng)網(wǎng)絡(luò)與通道和空間注意力機制的房顫預(yù)測模型研究()
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《福建醫(yī)藥雜志》[ISSN:1002-2600/CN:35-1071/R]

卷:
46
期數(shù):
2024年01期
頁碼:
1-4
欄目:
論著
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
文章編號:
1002-2600（2024）01-0001-04
作者:
王量弘1; 蔡冰潔1; 劉碩1; 楊濤1; 王新康2; 高潔2
1 福州大學(xué)物理與信息工程學(xué)院，福州 350108；2 福建省立醫(yī)院心電診斷科，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
關(guān)鍵詞:
心電信號; 房顫; 卷積神經(jīng)網(wǎng)絡(luò); 通道和空間注意力機制
Keywords:
ECG signals;  atrial fibrillation;  convolutional neural networks;  convolutional block attention module
分類號:
R541.7+5
DOI:
10.20148/j.fmj.2024.01.001
文獻標志碼:
A
摘要:
目的采用人工智能技術(shù)提出一種模型，以對房顫進行早期預(yù)防和診斷。方法提出一種基于卷積神經(jīng)網(wǎng)絡(luò)（convolutional neural network, CNN）與通道和空間注意力機制（convolutional block attention module，CBAM）的模型用于對房顫的診斷與預(yù)測。結(jié)果根據(jù)長期心房顫動數(shù)據(jù)庫、MIT-BIH心房顫動數(shù)據(jù)庫和MIT-BIH正常竇性心律數(shù)據(jù)庫的數(shù)據(jù), 提出的模型在全盲的情況下總體準確率達94.2%。結(jié)論提出的模型滿足了醫(yī)學(xué)心電圖解釋的需要，為房顫的預(yù)測研究提供了新思路。
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.

參考文獻/References:

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備注/Memo

備注/Memo:
基金項目：國家自然科學(xué)基金面上項目（61971140）
更新日期/Last Update: 2024-02-15