混合机器学习框架用于多阶段的帕金森病分类,使用持续的韩国元音的声学特征
S I M M Raton Mondol1, Ryul Kim2, Sangmin Lee1
1Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
这项研究通过分析语音,区分早期和高级阶段来增强帕金森病 (PD) 诊断. 听力障碍的声学分析在使用机器学习对PD阶段的分类方面取得了高准确性.
科学领域:
- 语音病理学 语音病理学
- 计算语言学计算语言学
- 神经学 神经学
背景情况:
- 现有的使用语音的帕金森病 (PD) 检测方法经常忽略特定阶段的语言障碍.
- 跨PD阶段的准确分类对于量身定制的治疗和管理至关重要.
研究的目的:
- 开发一个强大的机器学习管道来分类帕金森病 (PD) 阶段,使用失声的声学特征.
- 调查特定声学特征与Hoehn和Yahr PD阶段之间的相关性.
主要方法:
- 从持续的韩国元音 (/a/, /i/, /u/) 中提取了声障特征.
- 用于特征选择的变异F值的分析和用于类不平衡的合成少数人过量采样技术.
- 利用随机森林,SVM,kNN和MLP分类器进行稳健的模型评估.
主要成果:
- 实现了95.48%的准确性,用于早期和中期高级PD阶段.
- 达到86.62%的准确性为4阶段的PD分类和89.48%的准确性为3阶段的PD分类.
- 声学特征与PD进展之间有显著的相关性.
结论:
- 拟议的混合机器学习方法有效地使用声音失声特征对PD阶段进行了分类.
- 这种方法通过考虑帕金森病的特定阶段的声音变化,提高了诊断精度.
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