Risk Stratification Model of Sudden Cardiac Death - Details

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Indexed by：

EI Scopus

Abstract：

Current　sudden　cardiac　death　(SCD)　studies　mostly　use　traditional　machine　learning　algorithms　and　suffer　from　low　accuracy.　Deep　learning　has　a　promising　application　in　the　field　of　SCD　research.　The　study　extract　R-R　interval　and　R　amplitude　from　ECG　signals　as　inputs,　combined　convolutional　neural　network　and　gated　recurrent　unit,　and　take　full　advantage　of　hybrid　neural　network　structure　to　realize　the　risk　stratification　of　high-risk　patients　who　may　have　SCD　within　90　minutes,　with　the　highest　accuracy　of　95.33%.　©　2024　IEEE.

Keyword：

Adversarial machine learning Contrastive Learning Convolutional neural networks Recurrent neural networks

Community：

[ 1 ] [Ji, Tian-Yun]Fuzhou University, College of Physics and Information Engineering, Department of Microelectronics, Fuzhou, China
[ 2 ] [Xie, Chao-Xin]Fuzhou University, College of Physics and Information Engineering, Department of Microelectronics, Fuzhou, China
[ 3 ] [Yang, Tao]Fuzhou University, College of Physics and Information Engineering, Department of Microelectronics, Fuzhou, China
[ 4 ] [Kuo, I-Chun]Fuzhou University, College of Biological Science and Engineering, Fuzhou, China
[ 5 ] [Chen, Shih-Lun]Chung Yuan Christian University, Department of Electronic Engineering, Taoyuan City, Taiwan
[ 6 ] [Wang, Liang-Hung]Fuzhou University, College of Physics and Information Engineering, Department of Microelectronics, Fuzhou, China