基于改进MobileNet的ECG身份识别算法
ECG Identification Algorithm Based on Improved MobileNet
韦以嘉 1张烨菲 2张显飞 3赵治栋2
作者信息
- 1. 杭州电子科技大学通信工程学院,浙江杭州 310018
- 2. 杭州电子科技大学网络空间安全学院,浙江杭州 310018
- 3. 杭州电子科技大学电子信息学院,浙江杭州 310018
- 折叠
摘要
信息安全在当今社会显得愈发重要,基于心电(Electrocardiogram,ECG)信号的身份识别技术因其"活体"采集的高防伪性,呈现出了独特的优势.为了在移动环境下实现更高效快捷的身份识别,提出了一种基于稀疏卷积(Sparse convolution,SP)和轻量化网络MobileNet的深度迁移识别模型SP-MobileNet.首先对原始ECG信号进行预处理,采用小波软阈值消噪后并将其盲分割成信号片段,采用广义S变换得到ECG时频图作为网络输入;其次构建基于SP-MobileNet的ECG识别模型,引入MobileNet,修改其卷积层为稀疏卷积计算策略,通过迁移学习实现从导联Ⅱ采集的大样本ECG数据训练到指尖采集的小样本ECG识别的无缝连接.实验结果表明,该算法可以高效快捷地进行ECG身份识别,在PhysioNet/Cinc Challenge 2017数据集上分别实现了 98.00％的识别准确率和50.4 FPS的推理速度.
Abstract
Information security is becoming more and more important in society today.The identification technology based on the electrocardiogram(ECG)signal presents its unique advantages,due to its outstanding anti-counterfeiting performance on"living"detection.To achieve more efficient identification in mobile scenarios,a depth migration recognition model SP-MobileNet based on sparse convolution and lightweight network MobileNet is proposed.Firstly,the original ECG signal is pre-processed:denoised by wavelet soft threshold,blindly segmented into signal segments,and transformed into ECG time-frequency map as the input of the network by generalized S transform.Then,an ECG recognition model based on SP-MobileNet is built:a sparse convolution calculation strategy is adopted in the convolutional layers of MobileNet,along with transfer learning method,realizing the seamless connection from the large-sample ECG data training collected from Lead Ⅱ to the small-sample ECG recognition collected by fingertips.Experimental results indicate that the proposed algorithm can perform ECG identification efficiently and quickly,achieving a recognition accuracy of 98.00％and an inference speed of 50.4 FPS on the PhysioNet/Cinc Challenge 2017 dataset.
关键词
心电信号/身份识别/轻量型网络/稀疏卷积/迁移学习Key words
ECG signal/identity recognition/lightweight network/sparse convolution/transfer learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据