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基于希尔伯特黄变换和深度卷积神经网络的房颤检测

郭一楠 邵慧杰 巩敦卫 李海泉 陈丽

郭一楠, 邵慧杰, 巩敦卫, 李海泉, 陈丽. 基于希尔伯特黄变换和深度卷积神经网络的房颤检测[J]. 电子与信息学报, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171
引用本文: 郭一楠, 邵慧杰, 巩敦卫, 李海泉, 陈丽. 基于希尔伯特黄变换和深度卷积神经网络的房颤检测[J]. 电子与信息学报, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171
GUO Yinan, SHAO Huijie, GONG Dunwei, LI Haiquan, CHEN Li. Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171
Citation: GUO Yinan, SHAO Huijie, GONG Dunwei, LI Haiquan, CHEN Li. Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171

基于希尔伯特黄变换和深度卷积神经网络的房颤检测

doi: 10.11999/JEIT211171
基金项目: 国家自然科学基金(61973305),中国矿业大学中央高校基本科研业务费专项资金(2020ZDPY0302)
详细信息
    作者简介:

    郭一楠:女,1975年生,教授,研究方向为智能数据感知与分析、群智优化与控制

    邵慧杰:男,1997年生,硕士生,研究方向为模式识别与心电信号处理

    巩敦卫:男,1970年生,教授,研究方向为智能优化与控制

    李海泉:男,1978年生,主任医师,研究方向为呼吸系统疾病的诊断与治疗

    陈丽:女,1969年生,主任医师,研究方向为职业性尘肺的诊断治疗及呼吸系统疾病的诊断治疗

    通讯作者:

    巩敦卫 dwgong@vip.163.com

  • 中图分类号: R540.4+1; TN911.7

Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network

Funds: The National Natural Science Foundation of China (61973305), Fundamental Research Funds of China University of Mining and Technology (2020ZDPY0302)
  • 摘要: 房颤是一种常见的心律失常,其发病率会随着年龄增长而升高。因此,从心电(ECG)信号中尽早识别出房颤,有助于降低中风风险和心源性死亡率。为有效提高其检测准确率,该文提出一种基于希尔伯特黄变换(HHT)和深度卷积神经网络的房颤检测方法。1维的时域心电信号通过希尔伯特黄变换,转换为时频域信号,旨在通过时频分析,丰富原始信号的特征。进而,采用DenseNet深度卷积神经网络来处理精细的时频图,并在迭代过程中选出最佳检测模型。该方法获得的最佳检测模型在麻省理工学院-贝斯以色列医院(MIT-BIH)和2017年生理信号竞赛(2017 PhysioNet Challenge)的房颤数据集上分别取得了99.11%和97.25%的检测准确率。此外,该文将希尔伯特黄变换与其他时频分析方法以及稠密网络(DenseNet)与其他卷积神经网络进行了对比。相比于其他检测方法,实验结果表明希尔伯特黄变换和深度卷积神经网络(DCNN)为房颤检测提供了更加准确的识别方式。
  • 图  1  稠密块结构

    图  2  ECG片段

    图  3  不同变换方法的时频图

    图  4  3种时频分析和1维心电在验证集上的准确率曲线

    图  5  受干扰的ECG片段

    图  6  VGG, ResNet和DenseNet在验证集上的准确率曲线

    表  1  EMD算法步骤

    步骤 1 $r(t) = x(t)$
    步骤 2 $ s(t) = r(t) $
    步骤 3 求$ s(t) $的极大值和极小值。
    步骤 4 根据极大极小值分别计算上包络线$ {e_{\max }}(t) $和下包络线$ {e_{\min }}(t) $。
    步骤 5 计算两个包络线的均线$ m(t) = $$ [{e_{\max }}(t){\text{ + }}{e_{\min }}(t)]/2 $。
    步骤 6 计算$ h(t) = r(t) - m(t) $。如果$ h(t) $满足上述两个限制,则$ h(t) $为其中一个IMF,否则令$ s(t) = r(t) - h(t) $返回步骤3。
    步骤 7 计算$ r(t) = r(t) - s(t) $,如果${{r}}(t)$有超过两个极值点,返回步骤2去计算另一个IMF,否则分解结束。
    下载: 导出CSV

    表  2  DenseNet结构

    输出特征图结构
    Conv148×6237×7 conv, 64, stride=2, padding=1
    Pool124×3122×2 max pool
    Dense Block124×312$\left[ {\begin{array}{*{20}{c}} {{\text{1}} \times {\text{1}}\;{\text{conv}},\;128} \\ {3 \times 3\;{\text{conv,}}\;32,{\text{ padding = 1}}} \end{array}} \right] \times 6$
    Conv224×3121×1 conv, 128
    Pool212×1562×2 avg pool
    Dense Block212×156$ \left[ {\begin{array}{*{20}{c}} {{\text{1}} \times {\text{1}}\;{\text{conv}},\;128} \\ {3 \times 3\;{\text{conv,}}\;32,{\text{ padding = 1}}} \end{array}} \right] \times 12 $
    Conv312×1561×1 conv, 256
    Pool36×782×2 avg pool
    Dense Block36×78$\left[ {\begin{array}{*{20}{c}} {{\text{1}} \times {\text{1}}\;{\text{conv}},\;128} \\ {3 \times 3\;{\text{conv,}}\;32,{\text{ padding = 1}}} \end{array}} \right] \times 32$
    Conv46×781×1 conv, 640
    Pool43×392×2 avg pool
    Dense Block43×39$\left[ {\begin{array}{*{20}{c}} {{\text{1}} \times {\text{1}}\;{\text{conv}},\;128} \\ {3 \times 3\;{\text{conv,}}\;32,{\text{ padding = 1}}} \end{array}} \right] \times 32$
    Pool11×13×39 avg pool
    flatten1×1664
    Fully connected layer1×2
    下载: 导出CSV

    表  3  AFDB数据集

    训练集验证集测试集总计
    房颤244133052305130516
    窦性节律562247028702870280
    总计806371008010079100796
    下载: 导出CSV

    表  4  2017 PhysioNet Challenge数据集

    训练集验证集测试集总计
    房颤43835485485479
    窦性节律300123752375137515
    总计343954300429942994
    下载: 导出CSV

    表  5  运动伪迹时模型鲁棒性验证(%)

    对比算法ACCSPSE
    本文77.4084.6270.37
    随机森林60.3857.6962.96
    支持向量机56.6053.8559.26
    Adaboost62.2661.5462.96
    下载: 导出CSV

    表  6  电磁干扰时模型鲁棒性验证(%)

    对比算法ACCSPSE
    本文97.8598.0497.62
    随机森林93.9193.4694.44
    支持向量机87.8189.2986.33
    Adaboost91.4092.8689.93
    下载: 导出CSV

    表  7  不同算法的性能比较(%)

    数据集文献ACCSPSE
    AFDB文献[11]-99.2997.87
    文献[15]98.7997.8798.63
    文献[26]98.5198.7698.14
    文献[27]98.8498.7598.97
    文献[28]98.5198.6798.32
    文献[7]98.2195.55100
    文献[29]97.2197.0897.34
    文献[30]97.196.2998.17
    本文99.1199.2998.69
    2017 PhysioNet Challenge
    文献[31]96.9998.4281.97
    文献[32]-98.672.7
    文献[14]97.1998.8480.26
    本文97.2599.6880.66
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-26
  • 修回日期:  2021-12-24
  • 录用日期:  2021-12-27
  • 网络出版日期:  2022-01-04
  • 刊出日期:  2022-01-10

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