基于短时心电信号的身份识别方法

Biometric identification method based on short-term ECG

为了提高基于短时 (1 s) 心电信号进行身份识别的准确率,本文提出了一种残差块的一维卷积神经网络 (one-dimensional convolutional neural network,1D-CNN) 的短时心电信号身份识别方法。该方法采用快捷连接设 计以解决深层卷积网络随着卷积层数增加而性能退化的问题,并通过增加卷积层数和卷积核数量来保证网络能够 更充分地提取特征,进而提升网络的分类性能。本文方法在两个公开数据库心电数据库 (electrocardiogram identifi⁃ cation database,ECG-ID) 和德国联邦物理技术研究院心电图诊断数据库 (physikalish-technische bundesanstalt di⁃ agnostic ECG database,PTB) 进行了实验,当采用一个心动周期 (大约 1 s) 信号进行身份识别时,准确率分别达 到了 97.963%和 99.359%。实验结果表明本文方法可以有效地提高短时心电信号的身份识别的准确率。

To improve the accuracy of identification based on short-term(1 second)electrocardigram(ECG)signal,a short term ECG signal identification method based on residual block-based one-dimensional convolutional neural network one-dimensional convolutional neural network(1D-CNN)is proposed in this paper.A shortcut connection design is used to solve the problem of performance degradation of deep convolutional networks as the number of convolutional layers increas⁃es,and by increasing the number of convolutional layers and the number of convolution kernels,the network can more fully extract features,thereby improving the classification performance of the network.The method has been tested on two public databases electrocardiogram identification database(ECG-ID)and physikalish-technische bundesanstalt diagnostic ECG da⁃tabase(PTB).When a cardiac cycle(nearly 1 second)signal is used for identification,the accuracy rates reached 97.963%and 99.359%,respectively.Experimental results show that this method can effectively improve the accuracy of short-term ECG signal identification.