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使用客观语音分析技术进行临床诊断和评估多发性硬化症患者的语言障碍

Zeynep Z Sonkaya¹, Bilgin Özturk², Rıza Sonkaya²

¹Department of Experimental Linguistics, Ankara University, 06590 Ankara, Turkey.

|April 27, 2024

概括

定量语音分析准确地确定了多发性硬化症 (MS) 患者的语言障碍. K-最近邻近算法实现了95%的准确性,帮助MS诊断和监测.

科学领域:

神经学神经学
语音语言病理学语言病理学
生物医学工程生物医学工程

背景情况:

多发性硬化症 (MS) 是一种慢性的神经退行性中枢神经系统疾病,影响运动,感官,认知和语言功能.
对MS相关的语言障碍的客观识别可以帮助临床诊断和患者监测.

研究的目的:

用客观语音分析来调查MS患者的语言障碍.
为了确定最有效的分类算法来区分MS语音模式.

主要方法:

使用PRAAT语音分析程序,分析了20名多发性硬化患者和20名健康对照组的语音数据.
测试各种分类算法,以区分群体之间的语音特征.

主要成果:

该K-最近邻居 (K-NN) 算法证明了最高的分类准确率95%.
K-NN有效地区分了MS特征的病态语言声音和健康个体的声音.

结论:

客观语音分析,特别是K-NN,显示出对识别和监测MS的希望.
这些发现提供了MS患者语音特征的初步数据,支持进一步的研究.

关键词:

在MSMSMSMSMSMSMSMSMSMS中,我们可以看到定量语音分析是一种语言分析.语言障碍语言障碍语言障碍

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