基于深度堆栈网络的心电信号识别

ECG Signal Recognition Based on Deep Stacked Network

传统的心电信号识别算法依靠心电专家参与特征识别,费时费力,诊断成本高,心电信号形态复杂多样导致识别准确率低、适应性差。为解决上述问题,将栈式稀疏自编码器(SSAE,Stacked Sparse Autoencoder),与Softmax分类器相结合形成深度堆栈网络(DSN,Deep Stacked Network)完成对心电信号的自动识别。通过3个稀疏自编码器堆叠的方式完成心电信号特征提取,逐层刻画心电信号的高维特征,由Softmax分类器完成心电信号识别。详细评估了深度堆栈网络的模型特性,确定了该网络模型的超参数,训练集样本和测试集样本源于MIT-BIH数据库。实验结果表明采用本文所提方法对心电信号进行识别,总识别率达到99.69%,验证了所提方法的有效性。

The traditional electrocardiogram(ECG)signal recognition algorithms rely on ECG experts to participate in feature recognition,which is time-consuming and laborious with high diagnostic cost.Complex and diverse ECG signal patterns result in low recognition accuracy and poor adaptability.To solve the above problems,the stack Sparse Autoencoder was combined with the Softmax classifier to form a Deep stack Network to realize automatic recognition of ECG signals.The feature extraction of ECG signals was completed by stacking three sparse autoencoders,and the high-dimensional features of ECG signals were depicted layer by layer,and the ECG signals were identified by Softmax classifier.Detailed assessment of the model characteristic of Deep stacked Network,determine the super parameter of the network model,sample training set and test set samples from MIT/BIH database.The experimental results show that the total recognition rate of the proposed method is 99.69%,which verifies the effectiveness of the proposed method.