基于毫米波雷达的非接触式心电重构算法

罗景雪; 张远辉; 戴潇; 付铎; 刘康

doi:10.11959/j.issn.1000-0801.2024238

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基于毫米波雷达的非接触式心电重构算法

研究与开发 | 更新时间：2025-03-17

- 基于毫米波雷达的非接触式心电重构算法
- Non-contact ECG reconstruction algorithm based on millimeter wave radar
- 电信科学 2024年40卷第11期页码：50-65
- 作者机构：
  
  中国计量大学机电工程学院，浙江杭州 310018
- 作者简介：
  
  [ "罗景雪（1999- ），女，中国计量大学机电工程学院硕士生，主要研究方向为毫米波雷达信号处理、生命体征监测。" ]
  [ "张远辉（1982- ），男，博士，中国计量大学机电工程学院副教授，主要研究方向为毫米波雷达算法、图像处理技术。" ]
  [ "戴潇（1999- ），男，中国计量大学机电工程学院硕士生，主要研究方向为毫米波雷达信号处理。" ]
  [ "付铎（1992- ），男，博士，中国计量大学机电工程学院讲师，主要研究方向为不确定机械系统的动力学控制、汽车路径规划。" ]
  [ "刘康（1977- ），男，博士，中国计量大学机电工程学院教授，主要研究方向为毫米波雷达与无人驾驶技术。" ]
- 基金信息：
  
  浙江省自然科学基金资助项目(LY19F010007)
- DOI：10.11959/j.issn.1000-0801.2024238
  中图分类号： TN958
- 收稿日期：2024-08-16，
  
  修回日期：2024-10-09，
  
  纸质出版日期：2024-11-20
- 稿件说明：
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罗景雪,张远辉,戴潇等.基于毫米波雷达的非接触式心电重构算法[J].电信科学,2024,40(11):50-65.

LUO Jingxue,ZHANG Yuanhui,DAI Xiao,et al.Non-contact ECG reconstruction algorithm based on millimeter wave radar[J].Telecommunications Science,2024,40(11):50-65.
罗景雪,张远辉,戴潇等.基于毫米波雷达的非接触式心电重构算法[J].电信科学,2024,40(11):50-65. DOI： 10.11959/j.issn.1000-0801.2024238.

LUO Jingxue,ZHANG Yuanhui,DAI Xiao,et al.Non-contact ECG reconstruction algorithm based on millimeter wave radar[J].Telecommunications Science,2024,40(11):50-65. DOI： 10.11959/j.issn.1000-0801.2024238.

摘要

近年来，毫米波雷达信号在医疗监测领域的应用日益广泛，实现雷达信号到心电信号的精准映射已成为满足日常持续性非接触心电监测需求的关键挑战。详细介绍了毫米波雷达信号处理流程，探索了雷达信号与心电信号的细粒度映射关系，引入基于卷积块注意力机制模块（convolutional block attention module，CBAM）的卷积自编码器（convolutional autoencoder，CAE）与双向长短期记忆（bidirectional long short-term memory，BiLSTM）组合的CAE-BiLSTM深度学习网络，实现了雷达信号到心电图的非线性转换。实验结果表明，所提方法在形态学精度上的中位数为0.92，特征峰预测误差低于50 ms，显著增强了雷达信号与心电信号的映射关系，为非接触式心电信号的生成提供了新思路。

Abstract

With the wide application of millimeter-wave radar signals in medical monitoring

accurately mapping these signals to ECG signals has become a key challenge in meeting the needs for daily continuous non-contact ECG monitoring. The signal processing flow of millimeter-wave radar was introduced in detail

the fine-grained mapping relationship between radar signals and ECG signals was explored

and the nonlinear transformation from radar signals to electrocardiograms was achieved through the introduction of the CAE-BiLSTM deep learning network

which was a hybrid of a convolutional autoencoder (CAE) and bi-directional long short-term memory (BiLSTM)

incorporating the convolutional block attention module (CBAM).The results show that the median morphological accuracy of the proposed method is 0.92

and the feature peak prediction error is less than 50 ms. The proposed approach significantly enhances the mapping relationship between radar and ECG signals and offers a new idea for generating non-contact ECG signals.

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references

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