基于二维图像与迁移卷积神经网络的心律失常分类

Arrhythmia Classification Based on Two-Dimensional Image and Tranfer Convolutional Neural Network

心律失常的自动分类对心血管疾病的诊断和预防具有重要意义。传统分类方法需要对心电信号进行人工特征提取,这对分类准确度有很大的影响。针对该问题,提出一种基于二维图像与迁移卷积神经网络(TCNN)的分类方法。通过对心电信号进行格拉姆角场变换将其转换为二维图像,在保证心电图像完整性的同时,保留原始信号的时间依赖性。在此基础上,结合迁移学习的思想,设计结构简单且参数量较少的TCNN模型对心电图像进行分类。实验结果表明,该方法网络训练用时较少,并且分类总准确率达到99.82%,可实现对心律失常的有效分类。

The automatic classification of arrhythmia is very important for the diagnosis and prevention of cardiovascular diseases.Traditional classification methods require artificial feature extraction of ECG signals,which has a great impact on the accuracy of classification.To solve this problem,a classification method based on two dimensional image and Transfer Convolutional Neural Network(TCNN)is proposed.The ECG signal is transformed into a two-dimensional image by means of Gramian Angular Field.The integrity of the ECG image is guaranteed while the time dependence of the original signal is retained.On this basis,combined with the idea of transfer learning,a TCNN model with simple structure and fewer parameters is designed to classify the ECG images.The experimental results show that the network training time of this method is less,and the classification accuracy reaches 99.82%,which can realize the effective classification of arrhythmia.