声信号数据库 ALS 患者与不同严重程度的异性关节炎和健康对照的声音信号数据库
Raffaele Dubbioso1, Myriam Spisto2, Laura Verde3
1Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples, 80131, Italy.
Scientific data
|July 19, 2024
概括
这项研究介绍了VOC-ALS数据库,这是一个全面的语音录音集合,来自肌缩侧面硬化症 (ALS) 患者和健康对照者. 基本频率 (F0) 标准偏差的分析有效地识别了ALS患者及其失联症的严重程度.
科学领域:
- 神经学 神经学
- 语音语言病理学 语音语言病理学
- 生物医学工程 生物医学工程
背景情况:
- 肌缩侧面硬化 (ALS) 是一种进展性神经退行性疾病,影响运动神经元.
- 失联症是ALS的常见症状,显著影响语音可理解性和语音质量.
- 客观和定量语音分析对于监测疾病进展和帮助临床诊断至关重要.
研究的目的:
- 将公开可用的语音信号介绍到肌缩侧面硬化症 (VOC-ALS) 数据库中.
- 提供一个全面的数据集,用于研究ALS中的语音损伤识别.
- 调查语音指标的实用性,以识别ALS和评估失联症的严重程度.
主要方法:
- 使用Vox4Health智能手机应用程序从153名参与者 (102名ALS患者,51名健康对照) 获取了1224个语音信号.
- 记录持续的元音音和音节重复 (/pa/, /ta/, /ka/).
- 语音参数的计算,包括波与噪声比,基本频率 (F0) 平均值和标准偏差,动和闪.
主要成果:
- 自音和音节的F0标准偏差在识别患有ALS的个体方面表现出卓越的准确性.
- 在ALS患者中,F0标准偏差有效地区分了不同严重程度的脱动症.
- VOC-ALS数据库代表了迄今为止对ALS研究的最广泛的语音信号集合.
结论:
- VOC-ALS数据库为推进ALS语音分析研究提供了宝贵的资源.
- 基本频率标准偏差是一种有前途的生物标志物,用于早期检测和评估与ALS相关的骨关节障碍症的严重程度.
- 这一数据集支持开发用于ALS患者语音监控的临床应用.
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