通过自动语音分析检测多发性硬化症疲劳数据集

数据集获取（Figshare）

• 源站标题：Table_1_Detecting fatigue in multiple sclerosis through automatic speech analysis.DOCX
• DOI：10.3389/fnhum.2024.1449388.s001
• 发布日期：2024-09-13
• 文件数：1 个
• 文件总体积：695.83 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Detecting_fatigue_in_multiple_sclerosis_through_automatic_speech_analysis_DOCX/27012964

命令行下载

curl -L -o "Table_1_Detecting fatigue in multiple sclerosis through automatic speech analysis.DOCX" "https://ndownloader.figshare.com/files/49167883"

文件清单

• Table_1_Detecting fatigue in multiple sclerosis through automatic speech analysis.DOCX（695.83 KB）

文件总体积 695.83 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Multiple sclerosis (MS) is a chronic neuroinflammatory disease characterized by central nervous system demyelination and axonal degeneration. Fatigue affects a major portion of MS patients, significantly impairing their daily activities and quality of life. Despite its prevalence, the mechanisms underlying fatigue in MS are poorly understood, and measuring fatigue remains a challenging task. This study evaluates the efficacy of automated speech analysis in detecting fatigue in MS patients. MS patients underwent a detailed clinical assessment and performed a comprehensive speech protocol. Using features from three different free speech tasks and a proprietary cognition score, our support vector machine model achieved an AUC on the ROC of 0.74 in detecting fatigue. Using only free speech features evoked from a picture description task we obtained an AUC of 0.68. This indicates that specific free speech patterns can be useful in detecting fatigue. Moreover, cognitive fatigue was significantly associated with lower speech ratio in free speech (ρ = −0.283, p = 0.001), suggesting that it may represent a specific marker of fatigue in MS patients. Together, our results show that automated speech analysis, of a single narrative free speech task, offers an objective, ecologically valid and low-burden method for fatigue assessment. Speech analysis tools offer promising potential applications in clinical practice for improving disease monitoring and management.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnhum.2024.1449388.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Detecting_fatigue_in_multiple_sclerosis_through_automatic_speech_analysis_DOCX/27012964）。