混合预处理和组合分类用于使用多个语音信号数据库增强检测帕金森病
Ling-Chun Sun1, Chun-Wei Tseng2, Ke-Feng Lin2,3
1School of Medicine, National Defense Medical Center, Taipei, Taiwan.
Digital health
|June 30, 2025
概括
这项研究表明,集体机器学习模型可以使用语音记录有效检测帕金森病 (PD). 优化预处理是从各种声学数据集中准确检测PD的关键.
科学领域:
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
- 神经学 神经学
背景情况:
- 帕金森病 (PD) 的患病率在全球范围内正在上升.
- 对PD的声学记录数据库越来越多.
- 机器学习 (ML) 提供了从语音中检测PD的潜力.
研究的目的:
- 评估整体ML用于PD检测的可行性.
- 利用多种不同的声学数据集进行可靠的分析.
- 确定PD的关键声学特征.
主要方法:
- 采用混合式预处理框架:RobustScaler (缩放),ROS/SMOTE/RUS (采样),XGBoost/AdaBoost (分类).使用一个混合式预处理框架:ROS/SMOTE/RUS (采样).
- 在三个公共PD语音数据集上测试模型:MIU,UEX和UCI.
- 使用准确度,精度,回忆和F1得分来评估性能;使用SHAP分析来确定特征的重要性.
主要成果:
- 最佳预处理因数据集而异,但RobustScaler,组合采样和XGBoost/AdaBoost通常产生了优异的结果.
- 实现了高性能:MIU数据集 (97.37%准确度),UEX和UCI数据集 (100%准确度).
- 在SHAP分析中,Mel频率的塞普斯特尔系数被确定为PD相关的显著声学特征.
结论:
- 整体ML方法是可行的PD检测通过声学记录.
- 特定于数据集的预处理策略对于最佳性能至关重要.
- 确定了关键的声学特征,为开发基于语音的PD查工具提供了指导.
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