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基于语音卷积稀疏迁移学习和并行优选的帕金森病分类算法研究

张小恒 李勇明 王品 曾孝平 颜芳 张艳玲 承欧梅

张小恒, 李勇明, 王品, 曾孝平, 颜芳, 张艳玲, 承欧梅. 基于语音卷积稀疏迁移学习和并行优选的帕金森病分类算法研究[J]. 电子与信息学报, 2019, 41(7): 1641-1649. doi: 10.11999/JEIT180792
引用本文: 张小恒, 李勇明, 王品, 曾孝平, 颜芳, 张艳玲, 承欧梅. 基于语音卷积稀疏迁移学习和并行优选的帕金森病分类算法研究[J]. 电子与信息学报, 2019, 41(7): 1641-1649. doi: 10.11999/JEIT180792
Xiaoheng ZHANG, Yongming LI, Pin WANG, Xiaoping ZENG, Fang YAN, Yanling ZHANG, Oumei CHENG. Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1641-1649. doi: 10.11999/JEIT180792
Citation: Xiaoheng ZHANG, Yongming LI, Pin WANG, Xiaoping ZENG, Fang YAN, Yanling ZHANG, Oumei CHENG. Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1641-1649. doi: 10.11999/JEIT180792

基于语音卷积稀疏迁移学习和并行优选的帕金森病分类算法研究

doi: 10.11999/JEIT180792
基金项目: 国家自然科学基金(61771080, 61571069),重庆市基础与前沿研究项目(cstc2018jcyjAX0779, cstc2016jcyjA0043, cstc2016jcyjA0064, cstc2016jcyjA0134),重庆市教育委员会科学技术研究项目(KJ1603805),西南医院联合孵化项目(SWH2016LHYS-11),模式识别国家重点实验室开放课题基金(201800011)
详细信息
    作者简介:

    张小恒:男,1980年生,副教授,工程师,研究方向为人工智能、生物医学信号与信息处理

    李勇明:男,1976年生,教授,博士生导师,研究方向为人工智能、生物医学信号信息处理

    王品:女,1979年生,副教授,硕士生导师,研究方向为人工智能、生物医学信号信息处理

    曾孝平:男,1956年生,教授,博士生导师,研究方向为人工智能、信号与信息处理

    颜芳:男,1979年生,副教授,硕士生导师,研究方向为生物医学信号信息处理

    张艳玲:女,1974年生,教授,硕士生导师,研究方向为帕金森病诊疗、生物医学信号信息处理

    承欧梅:女,1968年生,教授,博士生导师,研究方向为帕金森病诊疗、生物医学信号信息处理

    通讯作者:

    李勇明 yongmingli@cqu.edu.cn

  • 中图分类号: TP391.42; R749

Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection

Funds: The National Natural Science Foundation of China (61771080, 61571069), The Chongqing Research Program of Basic Research and Frontier Technology(cstc2018jcyjAX0779, cstc2016jcyjA0043, cstc2016jcyjA0064, cstc2016jcyjA0134), The Chongqing Education Commission Science and Technology Research Program (KJ1603805), The Southwest Hospital Science and Technology Innovation Program (SWH2016LHYS-11), The Open Project Program of the National Laboratory of Pattern Recognition (201800011)
  • 摘要: 基于语音数据分析的帕金森病(PD)诊断存在样本量小、训练与测试数据分布差异明显的问题。为了解决这些问题,需要从降维和样本扩充两个方面同时进行。因此,该文提出结合加噪加权卷积稀疏迁移学习和样本特征并行优选的PD分类算法。该算法可从源域的公共语音库中学习有利于表达PD语音特征的有效结构信息,同时完成降维和样本间接扩充。样本特征并行优选考虑到了样本和语音特征间的关系,从而有助于获取高质量的特征。首先,对公共语音库进行特征提取构造公共特征库;然后,以公共特征库对PD目标域的训练数据集及测试数据集进行稀疏编码,这里分别采用传统稀疏编码(SC)与卷积稀疏编码(CSC)两种稀疏编码方法;接着,对编码后的语音样本段和特征数据进行同时优选;最后,采用支撑向量机(SVM)进行分类。实验结果表明,该算法针对受试者的分类准确率最高值达到了95.0%,均值达到了86.0%,较相关被比较算法有较大提高。此外,研究还发现,相较于传统稀疏编码方法,卷积稀疏编码更有利于提取PD语音数据的高层特征;同样,迁移学习也有利于提高该算法性能。
  • 图  1  本文算法使用迁移学习前后的平均分类准确率比较

    图  2  本文算法使用迁移学习前后的ROC曲线比较

    表  1  基于语音卷积稀疏迁移学习和并行优选的PD分类算法

     输入:公共数据集${\text{S}}$,目标领域数据集${\text{A}}$,样本总数H,每个样
    本的特征数N,受试者数M
     输出:分类准确率,灵敏度,特异度
     步骤:
       (1)对公共数据集${\text{S}}$叠加不同类型不同信噪比的噪声扩展其为
    集合${\text{S}}'$;
       (2)根据式(1),对集合${\text{S}}'$的语音样本提取特征构造特征库即
    源领域数据集${\text{Y}}$;
       (3)根据2.2.2节及2.2.3节,提取特征库${\text{Y}}$的字典(稀疏编
    码),卷积核(卷积稀疏编码);
       (4)根据1.2.4节,计算目标领域数据集${\text{A}}$的稀疏表达系数矩
    阵${\text{E}}$(稀疏编码)或特征图矩阵${\text{E}}$(卷积稀疏编码);
       (5)根据语音样本特征并行优选算法,将特征矩阵${\text{E}}$进行特征
    扩展为${\text{G}}$并归一化为${\text{G}}'$,并进行样本特征同时优选得矩阵${\text{P}}$;
       (6)基于SVM分类器进行受试者留一法(LOSO)分类计算。
    下载: 导出CSV

    表  2  各种算法分类结果对比(%)

    分类算法基于受试者的留一法
    准确率灵敏度特异度
    SVM(线性核函数)
    平均65.065.065.0
    最好65.065.065.0
    SVM(径向基核函数)
    平均67.580.055.0
    最好67.580.055.0
    文献[6]算法
    平均52.055.049.0
    最好85.085.090.0
    DBN算法
    平均54.652.456.8
    最好57.056.058.0
    CNN算法
    平均60.063.057.0
    最好65.061.069.0
    autoencoder+SVM(TL)
    平均72.575.070.0
    最好72.575.070.0
    autoencoder+SVM
    平均67.565.070.0
    最好67.565.070.0
    DBN+SVM(TL)
    平均55.560.051.0
    最好60.065.055.0
    DBN+SVM
    平均50.553.048.0
    最好57.565.050.0
    PD_SC&S2
    平均68.569.567.5
    最好90.085.095.0
    PD_SC&S2_TL
    平均81.079.582.5
    平均92.595.090.0
    PD_CSC&S2
    平均70.073.067.0
    最好75.074.076.0
    PD_CSC&S2_TL
    平均86.091.081.0
    最好95.0100.090.0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-09
  • 修回日期:  2019-01-28
  • 网络出版日期:  2019-02-23
  • 刊出日期:  2019-07-01

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