从咳记录中检测COVID-19使用袋词分类器
Irina Pavel1, Iulian B Ciocoiu1
1Faculty of Electronics, Telecommunications and Information Technology, "Gheorghe Asachi" Technical University of Iasi, Bd. Carol I 11A, 700050 Iasi, Romania.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究表明,使用稀疏编码和袋词分类器可靠地从咳记录中检测到COVID-19. 这种方法在各种特征提取和编码策略中提供了强大的性能.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 信号处理 信号处理
背景情况:
- 准确的COVID-19检测对公共卫生至关重要.
- 咳记录提供了一种非侵入性方法来检测疾病.
- 机器学习方法越来越多地用于分析生物医学信号.
研究的目的:
- 为了评估检测COVID-19的可靠性,使用咳记录和袋词分类器.
- 评估不同特征提取和编码策略对检测性能的影响.
- 将机器学习方法与卷积神经网络 (CNN) 进行基于咳的COVID-19检测.
主要方法:
- 使用词包分类器进行咳声音分析.
- 评估了四种不同的特征提取程序和四种编码策略.
- 评估了输入和输出融合方法.
- 对2D CNN进行了比较分析.
- 对COUGHVID和COVID-19声音数据集进行了实验.
主要成果:
- 稀疏编码在评估的指标 (AUC,精度,灵敏度,F1分数) 中表现出卓越的性能.
- 选择的编码策略显示出稳定性,无论特征类型或代码书尺寸.
- 研究了融合方法,以潜在地提高检测准确度.
- 词包分类器,特别是稀疏编码的词包分类器,证明了从咳检测COVID-19的有效性.
结论:
- 稀疏编码是一种高效的策略,用于检测COVID-19从咳声音使用袋词模型.
- 这些发现表明,使用机器学习进行咳分析可以成为可靠的诊断工具.
- 进一步的研究可以探索为现实世界的临床应用优化这些方法.
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