评估喉神经运动活动从声调
Pedro Gómez-Vilda1,2, Andrés Gómez-Rodellar3, Jiři Mekyska4
1NeuSpeLab, CTB, Universidad Politécnica de Madrid, 28220 Pozuelo de Alarcón, Madrid, Spain.
International journal of neural systems
|May 16, 2025
概括
帕金森病 (PD) 通过改变喉中的神经运动活动 (NMA) 来破坏言语. 这项研究使用差异神经运动活动 (DNMA) 分析和人工智能,根据语音模式,准确地区分PD患者与健康个体.
科学领域:
- 神经学 神经学
- 语音科学 语言科学
- 生物医学工程 生物医学工程
背景情况:
- 神经退行性运动障碍,如帕金森病 (PD),显著影响日常生活,言语产生尤其受到影响.
- 喉肌肉中神经运动活动 (NMA) 的变化会破坏声调的稳定性和规律性,影响声控制.
- 甲状腺 (紧张器) 和甲状腺 (放松器) 肌肉系统,由不同的神经运动通路调节,对于发音至关重要.
研究的目的:
- 为了研究帕金森病对与语音相关的神经运动活动 (NMA) 的影响.
- 开发一种方法来区分患有帕金森病 (PDP) 个体和健康对照参与者 (HCP) 的发音,使用差异神经运动活性 (DNMA).
- 应用可解释的AI (XAI) 方法来客观地对疾病进行分类和分层.
主要方法:
- 通过反向过来推导张力和放松通路中的差异神经运动活性 (DNMA) 的肌肉张力的间接估计.
- 使用詹森-香农分布分析了DNMA通道的幅度分布,将PDP和HCP与中年标准参与者 (RSP) 进行比较.
- 用96个音调样本的数据集来训练决策树分类器 (DTC) 来区分PDP和HCP,并进行10倍的交叉验证.
主要成果:
- 决策树分类器在区分男性 (90.63%) 和女性 (87.50%) 的PDP与HCP方面取得了很高的准确性.
- 分类性能指标包括高灵敏度 (例如,男性93.33%) 和特异性 (例如,男性88.23%).
- 该研究表明,使用可解释的人工智能,对PDP和HCP进行客观疾病分类和分层的潜力.
结论:
- 差异神经运动活动 (DNMA) 分析提供了一种可靠的方法来检测帕金森病的语言变化.
- 机器学习分类器,特别是决策树,可以有效地区分患有PD的个体与基于语音分析的健康对照.
- 这种方法为客观评估和对帕金森病患者分层提供了一个有希望的工具,利用可解释的AI.
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