ECG Identification Algorithm Based on CAE and Improved VGGNet

doi:10.19678/j.issn.1000-3428.0068320

Abstract

Abstract:

With the continuous development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, biometric identification techniques face the risk of information leakage. Electrocardiogram (ECG) signals have certain advantages in the field of biometric identification, owing to their highly anti-counterfeiting nature and in vivo recognition capabilities. In response to challenges such as traditional ECG recognition algorithms struggling to adapt to changing acquisition environments and low recognition stability and deep neural network-based ECG recognition algorithms with large model parameter sizes and difficulty in achieving fast response, an ECG identity recognition algorithm based on a Convolutional AutoEncoder (CAE) and an enhanced VGGNet is proposed in this study. First, a preprocessing method combining wavelet threshold denoising and single-heartbeat segmentation is designed to obtain clean single-cycle ECG signals as inputs. Then, a signal-mode feature extraction and dimensionality reduction module based on the CAE is constructed. Learning a lower-dimensional latent representation of the inputs. Finally, based on the optimized design of the VGGNet model, we studied the feature representation and obtained the results of individual recognition. The experimental results showed that the algorithm achieved a recognition accuracy of more than 96% for 189 testers in databases, including the MIT-BIH Arrhythmia Database, European ST-T Database, and ECG-ID. The recognition accuracy of the European ST-T Database is as high as 99.82%, which can realize individual identification with high accuracy and strong generalization ability.

Key words: Electrocardiogram(ECG), ECG recognition, Convolutional AutoEncoder(CAE), Residual Network(ResNet), signal preprocessing

摘要：

随着物联网技术和人工智能技术的不断发展, 生物识别技术面临着信息泄露的风险。心电图(ECG)信号因其活体识别的高防伪性在生物识别领域具有一定的优势。针对传统ECG识别算法不能适应多变的采集环境、识别稳定性不高以及基于深度神经网络的ECG识别算法模型参数量较大与难以实现快速响应等问题, 提出一种基于卷积自动编码器(CAE) 和改进式VGGNet的ECG身份识别算法。首先设计了结合小波阈值去噪和单心拍分割的预处理方法, 得到干净的单周期ECG信号作为模型输入。其次构建了基于CAE的信号模态特征提取与降维处理模块, 学习得到输入数据更小维度的潜在表示。最后基于VGGNet优化模型设计, 进一步深入学习特征表示, 得到个体识别的结果。实验结果表明, 该算法在MIT-BIH Arrhythmia Database、European ST-T Database和ECG-ID等数据库的189位测试者中实现了96%以上的识别精度, 其中European ST-T Database的识别精度高达99.82%, 可实现准确率较高、泛化能力较强的个体身份识别。

关键词: 心电图, ECG识别, 卷积自动编码器, 残差网络, 信号预处理

YAN Jie, ZHANG Yefei, ZHANG Xianfei. ECG Identification Algorithm Based on CAE and Improved VGGNet[J]. Computer Engineering, 2025, 51(1): 295-303.

严洁, 张烨菲, 张显飞. 基于CAE和改进式VGGNet的心电身份识别算法[J]. 计算机工程, 2025, 51(1): 295-303.

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Figures/Tables 14

Fig.1 The procedure of ECG identity recognition model

Fig.2 ECG signal segmentation

Fig.3 CAE model structure

Fig.4 The VGG-ResNet model of proposed in this paper

Fig.5 Comparison of ECG signal before and after denoising (from subject 100)

Fig.6 CAE model training process

Fig.7 Reconstruction of ECG signal

Fig.8 Classification performance of ECG identification model

Fig.9 The recognition performance of the proposed algorithm on different datasets

Fig.10 Confusion matrix

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