使用机器和深度学习方法对帕金森病的基于语音的检测:系统性审查
Hadi Sedigh Malekroodi1, Byeong-Il Lee1,2,3, Myunggi Yi1,2,4
1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
使用机器学习 (ML) 和深度学习 (DL) 的语音分析显示出早期帕金森病的前景.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 语音科学 语言科学
背景情况:
- 帕金森病 (PD) 是一种进展性神经退行性疾病,具有早期声乐障碍.
- 使用ML和DL进行语音分析为早期PD检测提供了一种非侵入性方法.
研究的目的:
- 系统地审查基于语音的PD检测的ML和DL的最新进展.
- 为了确定当前的挑战和未来的方向在该领域.
主要方法:
- 在主要数据库中对2020-2025年研究的系统文献综述.
- 分析了69项研究,重点关注数据集,语音任务,特征提取,模型和验证.
- 经典ML和DL模型性能的比较评估.
主要成果:
- 经典的ML模型 (SVM,RF) 在小数据集上表现良好.
- 深度学习 (CNN,RNN,变压器) 显示出更大的稳定性和可扩展性.
- 挑战包括数据集异质性,类不平衡和不一致的验证.
结论:
- 该领域正在向自我监督学习转变,以获得更好的概括性.
- 未来的进展需要大量的多语言数据集,标准化的协议和可解释的AI用于临床翻译.
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