基于原始波形的端到端阿尔茨海默症检测方法

陈旭初; 张卫强; 马勇

doi:10.12263/DZXB.20220162

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基于原始波形的端到端阿尔茨海默症检测方法

学术论文 | 更新时间：2025-12-11

- 基于原始波形的端到端阿尔茨海默症检测方法
- Raw Waveform-Based End-to-End Alzheimer's Disease Detection Method
- 电子学报 2023年51卷第12期页码：3582-3590
- 作者机构：
  
  1.清华大学电子工程系，北京 100084
  2.江苏师范大学语言科学与艺术学院，江苏徐州 221009
- 作者简介：
  
  [ "陈旭初男，1992年12月出生于河南省驻马店市.现为清华大学电子工程系在读硕士研究生.主要研究方向为音频事件检测、情感识别. E-mail: chen-xc20@mails.tsinghua.edu.cn" ]
  [ "张卫强（通讯作者）男，1979年1月出生于河北省雄县.2002年于中国石油大学应用物理系获学士学位，2005年于北京理工大学电子工程系获硕士学位，2009年于清华大学电子工程系获博士学位，2017年斯坦福大学访问学者，现为清华大学电子工程系副研究员.主要研究方向为语音与音频信号处理. E-mail: wqzhang@tsinghua.edu.cn" ]
  [ "马勇男，1977年3月出生于江苏省新沂市.2015年于北京工业大学电子信息与控制工程学院获得博士学位.现为江苏师范大学讲师，从事语音与音频信号处理方面的研究工作. E-mail: may@jsnu.edu.cn" ]
- 基金信息：
  
  NSFC-通用技术基础研究联合重点基金(U1836219)
- DOI：10.12263/DZXB.20220162
  中图分类号： TP391.5
- 收稿：2022-02-10，
  
  修回：2022-07-30，
  
  纸质出版：2023-12-25
- 稿件说明：
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陈旭初,张卫强,马勇.基于原始波形的端到端阿尔茨海默症检测方法[J].电子学报,2023,51(12):3582-3590.

CHEN Xu-chu,ZHANG Wei-qiang,MA Yong.Raw Waveform-Based End-to-End Alzheimer's Disease Detection Method[J].ACTA ELECTRONICA SINICA,2023,51(12):3582-3590.
陈旭初,张卫强,马勇.基于原始波形的端到端阿尔茨海默症检测方法[J].电子学报,2023,51(12):3582-3590. DOI： 10.12263/DZXB.20220162.

CHEN Xu-chu,ZHANG Wei-qiang,MA Yong.Raw Waveform-Based End-to-End Alzheimer's Disease Detection Method[J].ACTA ELECTRONICA SINICA,2023,51(12):3582-3590. DOI： 10.12263/DZXB.20220162.

摘要

阿尔茨海默症（Alzheimer’s Disease，AD）是一种退行性疾病，随着病情加重，患者的语言能力逐渐减弱.目前已经有研究者使用梅尔谱图、梅尔频率倒谱系数（Mel Frequency Cepstral Coefficient，MFCC）等声学特征对阿尔茨海默症患者和健康人进行分类，但是对于使用神经网络从原始波形提取特征进行阿尔茨海默症检测还缺少进一步的探索.本文提出一种基于原始波形的端到端阿尔茨海默症检测方法.该方法使用一维卷积从原始波形中提取时间维度特征，并使用含有膨胀卷积的残差块提取更复杂的特征.为进一步提高性能，在残差块中引入挤压-激励模块.在全国人机语音通讯学术会议（National Conference on Man-Machine Speech Communication，NCMMSC）2021 AD数据集上，本文提出的模型在长音频测试集、短音频测试集分别达到了86.55%和81.35%的准确率，比基线系统分别提高了6.75%、7.35%.在INTERSPEECH2020 ADReSS数据集上，模型的准确率为66.67%，比基线系统提高4.17%.

Abstract

Alzheimer's disease (AD) is a degenerative disease

as the disease worsens

the patient's language ability gradually decreases. Some researchers have already used acoustic features such as Mel spectrogram and Mel frequency cepstral coefficient (MFCC) to classify AD patients and healthy individuals

but there is a lack of further exploration on using neural networks to extract features from raw waveforms for AD detection. In this paper

we propose an end-to-end AD detection method based on raw waveforms. The method uses one-dimensional convolution to extract time-dimensional features from the original waveform and uses a residual block containing an inflated convolution to extract more complex features. To further improve performance

the squeeze-and-excitation block is introduced into the residual block. On the national conference on man-machine speech communication (NCMMSC) 2021 AD dataset

the model proposed in this paper achieves 86.55% and 81.35% accuracy on the long audio test set and short audio test set

respectively

which is 6.75% and 7.35% better than the baseline system

respectively. On the INTERSPEECH2020 ADReSS dataset

the accuracy of the model is 66.67%

an improvement of 4.17% over the baseline system.

关键词

Keywords

references

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