基于毫米波雷达微动信号和脉搏波数据融合的睡眠呼吸暂停低通气综合征筛查技术

赵翔 王威 李晨洋 关建 李刚

赵翔, 王威, 李晨洋, 等. 基于毫米波雷达微动信号和脉搏波数据融合的睡眠呼吸暂停低通气综合征筛查技术[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24107
引用本文: 赵翔, 王威, 李晨洋, 等. 基于毫米波雷达微动信号和脉搏波数据融合的睡眠呼吸暂停低通气综合征筛查技术[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24107
ZHAO Xiang, WANG Wei, LI Chenyang, et al. Diagnosis of sleep apnea hypopnea syndrome using fusion of micro-motion signals from millimeter-wave radar and pulse wave data[J]. Journal of Radars, in press. doi: 10.12000/JR24107
Citation: ZHAO Xiang, WANG Wei, LI Chenyang, et al. Diagnosis of sleep apnea hypopnea syndrome using fusion of micro-motion signals from millimeter-wave radar and pulse wave data[J]. Journal of Radars, in press. doi: 10.12000/JR24107

基于毫米波雷达微动信号和脉搏波数据融合的睡眠呼吸暂停低通气综合征筛查技术

DOI: 10.12000/JR24107
基金项目: 国家杰出青年科学基金(61925106)
详细信息
    作者简介:

    赵 翔,硕士生,主要研究方向为毫米波雷达AI感知技术、数据驱动医疗健康

    王 威,博士生,主要研究方向为毫米波雷达AI感知技术、数据驱动医疗健康

    李晨洋,工程师,主要研究方向为睡眠监测系统开发、生命信息检测技术等

    关 建,主任医师,主要研究方向为阻塞性睡眠呼吸暂停综合征的基础与临床科学研究

    李 刚,博士,教授,博士生导师,主要研究方向为雷达信号处理、遥感、多源信息融合、数据驱动医疗健康等

    通讯作者:

    李刚 gangli@mail.tsinghua.edu.cn

  • 责任主编:洪弘 Corresponding Editor: HONG Hong
  • 中图分类号: TN974; TN95

Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data

Funds: The National Science Fund for Distinguished Young Scholars (61925106)
More Information
  • 摘要: 睡眠呼吸暂停低通气综合征(SAHS)是一种常见的慢性睡眠呼吸障碍疾病,严重影响患者的睡眠质量和身体健康。该文提出了一种基于多源信号融合的睡眠呼吸暂停与低通气检测框架,通过融合毫米波雷达微动信号与光电容积脉搏波(PPG)描记法的脉搏波数据,实现高可靠的轻接触式睡眠呼吸暂停低通气综合征的诊断,以解决传统医学上依赖多导睡眠图(PSG)进行睡眠监测时舒适度差、成本高等缺点。研究中,为兼顾睡眠呼吸异常事件检测的准确率和鲁棒性,该文提出了一种雷达、脉搏波数据预处理算法得到信号中的时频信息和人工特征,并设计了用于将两类信号融合的深度神经网络,以实现对睡眠呼吸暂停和低通气事件的精准识别,从而估算呼吸暂停低通气指数(AHI),用于对患者的睡眠呼吸异常严重程度进行定量评估。基于上海交通大学医学院附属第六人民医院临床试验数据集的实验结果表明,该文所提方案估算的AHI与金标准PSG的相关系数达到了0.93,一致性良好,有潜力普及成为家用睡眠呼吸监护的工具,并起到睡眠呼吸暂停低通气综合征初步筛查的作用。

     

  • 图  1  医疗设备图

    Figure  1.  Medical device diagram

    图  2  实验数据采集场景示意图

    Figure  2.  Schematic diagram of data collection scene

    图  3  雷达信号预处理流程

    Figure  3.  Preprocessing flow for radar signal

    图  4  脉搏波信号预处理流程

    Figure  4.  Preprocessing algorithm flow for pulse wave

    图  5  预处理结果可视化

    Figure  5.  Visualization of preprocessing results

    图  6  多源信号融合框图

    Figure  6.  Multisource signal fusion diagram

    图  7  二维谱图信号特征提取和融合模块

    Figure  7.  Module for signal feature extraction and fusion in 2D spectrogram

    图  8  通道注意力模块

    Figure  8.  Channel attention module

    图  9  多尺度特征提取模块

    Figure  9.  Multi-scale feature extraction module

    图  10  一维时序信号特征提取和融合模块

    Figure  10.  Module for feature extraction and fusion of 1D temporal signals

    图  11  决策层融合模块

    Figure  11.  Decision layer fusion module

    图  12  不同传感器实验预测结果对比(N表示正常;A表示呼吸暂停;H表示低通气)

    Figure  12.  Comparison of experimental prediction results from different sensors (N: Normal; A: Apnea; H: Hypopnea)

    图  13  雷达与脉搏波特征图对比

    Figure  13.  Comparison of radar and pulse wave feature maps

    图  14  PSG标注与预测结果对比(N表示正常;A表示呼吸暂停;H表示低通气)

    Figure  14.  Comparison of PSG annotations and predicted results (N: Normal; A: Apnea; H: Hypopnea)

    图  15  基于雷达和脉搏波融合检测的AHI与PSG参考值的对比

    Figure  15.  Comparison of AHI between radar-PPG signal fusion and reference values from PSG

    图  16  SAHS病情分级混淆矩阵

    Figure  16.  Confusion matrix for grading the severity of SAHS

    表  1  FMCW雷达基本参数

    Table  1.   Basic parameters of FMCW radar

    参数 数值
    起始频率$ {f_{\text{c}}} $ $ 60{\text{ GHz}} $
    线性调频带宽B $ {\text{3 GHz}} $
    扫频周期T $ {\text{2 ms}} $
    快时间实际采样点数N $ 256 $
    快时间采样频率$ {f_{\text{s}}} $ $ 1000{\text{ kHz}} $
    距离分辨率$ \Delta R $ $ {\text{5 cm}} $
    下载: 导出CSV

    表  2  脉搏血氧仪基本参数

    Table  2.   Basic parameters of the pulse oximeter

    参数 数值
    红光波长 $ 660 \pm {\text{3 nm}} $
    红外光波长 $ {\text{905}} \pm {\text{10 nm}} $
    红光辐射功率 $ {\text{3}}{\text{.2}} \times 1{{\text{0}}^{{{ - 3}}}}{\text{ W}} $
    红外光辐射功率 $ {\text{2}}{\text{.4}} \times 1{{\text{0}}^{{{ - 3}}}}{\text{ W}} $
    采样频率 $ {\text{128 Hz}} $
    下载: 导出CSV

    1  脉搏波间期提取算法

    1.   Pulse wave interval extraction algorithm

     输入:脉搏波滤波信号$ {P_{\text{B}}}(t) $
     输出:脉搏波间期$ R(t) $
     1.将信号取平方,得到对应点的能量信号$ {P_{\text{w}}}(t) $
     2.以脉搏波收缩波的平均持续时间$ {T_1} $为滑窗长度,对信号进行平
     滑以凸显收缩波,得到信号$ {x_{{\text{peak}}}}(t) $。
     3.以脉搏波的平均持续时间$ {T_2} $为滑窗长度,对信号进行平滑以压
     缩舒张波,得到信号$ {x_{{\text{beat}}}}(t) $。
     4.利用信号$ {x_{{\text{beat}}}}(t) $与能量信号$ {P_{\text{w}}}(t) $加权求和得各个时刻阈值
     $ {x_{{\text{thr}}}}(t) $。
     5.比较信号$ {x_{{\text{peak}}}}(t) $和阈值$ {x_{{\text{thr}}}}(t) $,定位收缩波。
     6.寻找收缩波时间段内的最大极值点作为收缩波的峰值点,从
     而确定脉搏间期$ R(t) $。
    下载: 导出CSV

    表  3  时序信号与谱图信号对比实验

    Table  3.   Comparison experiment between time-domain signals and spectrogram signals

    信号 N A H
    pre rec F1 pre rec F1 pre rec F1
    时序信号 0.859 0.975 0.913 0.863 0.508 0.639 0.616 0.361 0.455
    谱图信号 0.928 0.945 0.936 0.731 0.848 0.785 0.558 0.586 0.572
    时序信号+谱图信号 0.961 0.934 0.947 0.751 0.895 0.817 0.627 0.650 0.638
    注:加粗项表示最优结果。
    下载: 导出CSV

    表  4  雷达与脉搏波对比实验

    Table  4.   Comparison experiment between radar signals and pulse wave signals

    传感器 N A H
    pre rec F1 pre rec F1 pre rec F1
    PPG 0.834 0.971 0.897 0.816 0.681 0.742 0.652 0.462 0.541
    Radar 0.909 0.902 0.906 0.714 0.826 0.766 0.506 0.549 0.527
    PPG+Radar 0.961 0.934 0.947 0.751 0.895 0.817 0.627 0.650 0.638
    注:加粗项表示最优结果。
    下载: 导出CSV

    表  5  现有研究结果性能比较

    Table  5.   Comparative performance with existing research findings

    方法 数据集人数 传感器 呼吸监测仪 Bland-Altman 线性拟合系数R
    Kang, 2020[18] (CFAR) 94 (23, 24, 14, 33) 6.5~8.0 GHz UWB PSG –2.8 (–21.7, +16.1) 0.96
    Kwon, 2022[20] (CNN+LSTM) 36 (6, 9, 8, 11) 6.5~8.0 GHz UWB PSG –2.0 (–14.6, +10.7) 0.97
    Hayano, 2020[42] (ACAT) 41 (11, 8, 16, 6) PPG PSG 0.81
    本文方法(雷达+脉搏波) 86 (27, 19, 14, 26) 60~63 GHz FMCW+PPG PSG 1.38 (–7.18, +9.94) 0.93
    下载: 导出CSV

    表  6  对照实验结果

    Table  6.   The results of control experiment

    传感器 组内相关系数 诊断阈值(次/h) 敏感度(%) 特异度(%) 准确率(%) Kappa系数
    PPG 0.89 5 74.07 89.83 84.88 0.6455
    15 89.13 90.00 89.53 0.7901
    30 95.00 80.77 90.69 0.7746
    Radar 0.93 5 66.67 89.83 82.56 0.5825
    15 91.30 92.50 91.86 0.8367
    30 95.00 88.46 93.02 0.8346
    Radar+PPG 0.98 5 96.29 93.22 94.19 0.8690
    15 97.83 92.50 95.35 0.9062
    30 96.67 96.15 96.51 0.9182
    注:加粗项表示最优结果。
    下载: 导出CSV
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  • 收稿日期:  2024-05-30
  • 修回日期:  2024-09-05
  • 网络出版日期:  2024-09-30

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