• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >Classification of parkinson's disease patients using nonlinear phonetic features and mel-frequency cepstral analysis
【24h】

Classification of parkinson's disease patients using nonlinear phonetic features and mel-frequency cepstral analysis

机译：使用非线性语音特征和梅尔频率倒谱分析对帕金森氏病患者进行分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a combinational feature extraction approach using voice utterances for discriminating Parkinson's disease (PD) patients from healthy people. The proposed feature set consists of seven nonlinear phonetic features and 13 usual Mel-frequency cepstral coefficients (MFCCs). In this research, two new features-EDC-PIS (energy distribution coefficient of peak index series) and EDC-PMS (energy distribution coefficient of peak magnitude series)-were introduced, which are robust to many uncontrollable confounding effects such as noisy environments. The nonlinear phonetic features comprise recurrent period density entropy (RPDE), detrended fluctuation analysis (DFA), noise-to-harmonic ratio (NHR), fractal dimension (FD), pitch period entropy (PPE), EDC-PIS, and EDC-PMS. MFCC features have been widely used in voice processing tasks and therefore are good candidates to be used for the voice processing of PD subjects. The dataset used was composed of a range of 200 voice utterances from 25 PD subjects with different severity levels, and 10 normal persons. Using voice utterances from healthy and PD subjects, a 20-dimensional final feature set using MFCCs and nonlinear features is composed. Finally, a multilayer perceptron (MLP) neural network classifier with one hidden layer was used to discriminate PD subjects. Also, the proposed system was used for classification of mild and severe PD subjects. We obtained 97.5% overall correct classification performance for the discrimination of PD. In addition, we obtained 95.5% overall accuracy for the discrimination of mild and severe PD subjects.
机译：本文提出一种使用语音发声的组合特征提取方法，以区分帕金森氏病（PD）患者与健康人。拟议的特征集包括七个非线性语音特征和13个通常的梅尔频率倒谱系数（MFCC）。在这项研究中，引入了两个新功能-峰值指数系列的能量分布系数EDS-PIS和峰值大小系列的能量分布系数EDC-PMS，它们对许多不可控制的混杂效应（例如嘈杂的环境）具有鲁棒性。非线性语音特征包括重复周期密度熵（RPDE），去趋势波动分析（DFA），信噪比（NHR），分形维数（FD），基音周期熵（PPE），EDC-PIS和EDC- PMS。 MFCC功能已广泛用于语音处理任务中，因此是用于PD主体的语音处理的良好候选者。所使用的数据集由来自25位不同严重程度的PD受试者和10位正常人的200种语音组成。使用来自健康和PD受试者的语音，组成了使用MFCC和非线性特征的20维最终特征集。最后，使用具有一个隐藏层的多层感知器（MLP）神经网络分类器来区分PD对象。而且，所提出的系统用于对轻度和重度PD受试者进行分类。对于PD的区分，我们获得了97.5％的总体正确分类性能。此外，对于轻度和重度PD受试者，我们获得了95.5％的总体准确率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号