自动化与信息工程2025，Vol.46Issue(2)：48-53,6.DOI:10.12475/aie.20250207
粗糙集与径向基函数神经网络融合的除颤结果预测算法
Defibrillation Outcome Prediction Algorithm Integrating Rough Set and Radial Basis Function Neural Network
摘要
Abstract
Multiple combinations of ventricular fibrillation electrocardiogram features can improve the accuracy of defibrilla-tion outcome prediction.However,when algorithms are deployed on embedded devices with limited hardware resources,computational capacity becomes constrained.Additionally,there may be redundancy among ventricular fibrillation electrocardiogram features,leading to inefficient use of computational resources.To address these issues,this study proposes a defibrillation outcome prediction algorithm that integrates rough set theory and a radial basis function(RBF)neural network.First,ventricular fibrillation electrocar-diogram features are extracted from the ventricular fibrillation electrocardiogram waveform dataset.Then,an attribute reduction algorithm based on attribute importance is applied to reduce redundant features.Finally,the reduced ventricular fibrillation electro-cardiogram feature dataset is used to train the RBF neural network for defibrillation outcome prediction.Experimental results demon-strate that this algorithm effectively reduces model storage requirements while improving prediction speed and accuracy.关键词
粗糙集/径向基函数神经网络/除颤结果预测/室颤心电特征/属性约简Key words
rough set/radial basis function neural network/defibrillation outcome prediction/ventricular fibrillation electrocardiogram features/attribute reduction分类
基础医学引用本文复制引用
陈其琛,杨其宇..粗糙集与径向基函数神经网络融合的除颤结果预测算法[J].自动化与信息工程,2025,46(2):48-53,6.
