基于CvT-13和多模态图像融合的心电分类算法

Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion

心电(ECG)信号是心律失常和心肌梗死诊断的重要依据。为进一步提升心律失常和心肌梗死分类效果,提出了一种基于Convolutional vision Transformer(CvT)和多模态图像融合的心电分类算法。通过格拉姆求和角场(GASF)、格拉姆差分角场(GADF)和递归图(RP)将ECG一维信号转化成三种不同模态的二维图像,并融合生成包含了更多特征的多模态融合图像。CvT-13模型对融合后的图像进行处理可以兼顾局部和全局信息,从而有效提升了分类性能。在MIT-BIH心律失常数据集和PTB心肌梗死数据集上,该算法对五种心律失常分类的综合准确率达到99.9%,对心肌梗死分类的综合准确率达到99.8%。实验表明,高精度计算机辅助的智能分类方法具有一定的优越性,可以有效提高心律失常以及心肌梗死等心脏疾病的诊断效率。

Electrocardiogram(ECG)signal is an important basis for the diagnosis of arrhythmia and myocardial infarction.In order to further improve the classification effect of arrhythmia and myocardial infarction,an ECG classification algorithm based on Convolutional vision Transformer(CvT)and multimodal image fusion was proposed.Through Gramian summation angular field(GASF),Gramian difference angular field(GADF)and recurrence plot(RP),the one-dimensional ECG signal was converted into three different modes of two-dimensional images,and fused into a multimodal fusion image containing more features.The CvT-13 model could take into account local and global information when processing the fused image,thus effectively improving the classification performance.On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset,the algorithm achieved a combined accuracy of 99.9%for the classification of five arrhythmias and 99.8%for the classification of myocardial infarction.The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.