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Updated: Jun 5, 2025

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
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用音频谱变压器对帕金森病的声声生物标志物进行分类.

Nuwan Madusanka1, Byeong-Il Lee2

  • 1Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of Korea; Department of Software Engineering, Sri Lanka Technological Campus (SLTC), Padukka 10500, Sri Lanka.

Journal of voice : official journal of the Voice Foundation
|December 12, 2024
PubMed
概括

音频谱变压器 (AST) 模型有效地使用语音生物标志物检测帕金森病 (PD). 这种先进的AI显示出高准确性和跨语言概括性,提供了一个有希望的非侵入性诊断工具.

关键词:
帕金森病 语音生物标志物 自我注意力机制 音频谱图变压器

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科学领域:

  • 神经学 神经学
  • 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) 是一种神经退行性疾病,影响运动和非运动功能,包括言语.
  • 声声生物标志物为PD检测提供了一个潜在的非侵入性方法.
  • 传统的深度学习模型在捕捉PD中微妙的语言障碍方面存在局限性.

研究的目的:

  • 评估音频谱变压器 (AST) 模型在使用声声生物标志物检测帕金森病时的有效性.
  • 为了比较AST的性能与已建立的PD检测深度学习架构.
  • 评估AST模型在PD语音分析中跨语言概括的能力.

主要方法:

  • 在两个数据集 (PC-GITA和ITA) 中分析了150名参与者的语音记录 (PD和健康对照).
  • 使用了音频谱变压器 (AST) 模型,并与VGG16,VGG19,ResNet18,ResNet34,视觉变压器和swin变压器进行了比较.
  • 标准的音频预处理包括将采样率标准化为16kHz和幅度规范化.

主要成果:

  • AST模型实现了更高的分类准确性:97.14% (ITA),91.67% (PC-GITA) 和92.73% (组合数据集).
  • AST在准确性方面超过了传统架构的5%-10%,证明了强大的跨语言通用化.
  • 在语音任务中观察到一致的表现,在持续的母音分析中,高精度 (0.97) 和回忆 (0.96).

结论:

  • 通过语音分析,AST模型提供了一种可靠且非侵入性的方法来检测帕金森病.
  • 该模型具有强大的跨语言概括性,这表明其具有广泛临床应用的潜力.
  • 建议在不同人群中进行进一步的验证,以便在临床上实施基于AST的PD检测.