基于深度学习的指-桡端脉搏波信号转换方法  

Deep learning-based method for finger-radial PPG signal transferring

作　　者：周韡鼎 陈兆学[1] ZHOU Weiding;CHEN Zhaoxue(School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学健康科学与工程学院,上海200093

出　　处：《中国医学物理学杂志》2023年第2期202-207,共6页Chinese Journal of Medical Physics

基　　金：“国家中医药多学科交叉创新团队”项目(ZYYCXTD-D-202208)。

摘　　要：针对目前市面上大多数脉搏波检测仪器检测的是指端脉搏波信号,提出一种基于卷积神经网络的指-桡端脉搏波信号转换方法,在仅获取指端脉搏波信号的情况下得到对应的桡动脉脉搏波信号。该方法主要由一维卷积神经网络通过端到端的训练实现,模型包含编码器、解码器和跳跃连接3个部分,通过编码器网络提取指端脉搏波信号的特征,再通过解码器网络将特征图进行扩展,并且利用跳跃连接的方式实现特征图的融合。采集60份指端和桡端的脉搏波信号进行实验,并与传递函数模型和弹性腔模型进行对比。实验结果表明,该模型转换所得的桡端脉搏波信号在MAE和PRD的指标上分别达到1.4%±0.3%和3.6%±1.2%,优于其他模型。研究表明,该模型能够较精确地实现指端脉搏波信号到桡端脉搏波信号的转化。In view of the fact that most pulse wave detection instruments currently exist on the market detect the photoplethysmography(PPG) signal from the fingertip, a convolutional neural network based finger-radial PPG signal transferring method is proposed for obtaining the corresponding radial PPG signal under the condition that only the finger PPG signal is available. The method is mainly realized by one-dimension convolutional neural network through end-to-end training, and the model is composed of 3 parts, namely encoder, decoder and skip connection. The features of the fingertip pulse wave signal are extracted through the encoder network, and then the feature maps are expanded through the decoder network while the feature maps are concatenated by skip connection. In the study, 60 finger and radial PPG signals are collected for experiments, and the proposed method is compared with transfer function model and windkessel model. The results show that the MAE and PRD of the radial PPG signal reconstructed by the proposed model reached 1.4%±0.3% and3.6% ±1.2%, respectively, indicating that the proposed method is superior to the other models. It is demonstrated that the proposed model can accurately transferring the finger PPG signal to the radial PPG signal.

关 键 词：光电容积描记信号 脉搏波 脉诊 深度学习 

分 类 号：R318[医药卫生—生物医学工程] TP181[医药卫生—基础医学]