机器学习和深度学习模型的再培训和评估,用于从EEG数据中进行发作分类
Juan Pablo Carvajal-Dossman1, Laura Guio2, Danilo García-Orjuela3
1System and computing engineering department, Universidad de Los Andes, Bogota, Colombia.
Scientific reports
|May 2, 2025
概括
使用机器学习在脑电图 (EEG) 中自动检测发作是有前途的. 然而,受过公共数据训练的模型在现实世界的临床准确性方面扎,这凸显了需要进一步发展的需要.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 电脑电图 (EEG) 对于的诊断至关重要.
- 在EEG数据中手动注释发作是耗时的.
- 机器学习模型旨在自动检测发作,但在临床实践中存在准确性差距.
研究的目的:
- 复制和评估各种机器学习模型的准确性,用于EEG中自动检测发作.
- 在公共数据集和本地患者数据中比较模型性能.
- 确定有效的模型,并为临床整合提供数据.
主要方法:
- 复制和评估了许多机器学习模型,包括深度学习网络.
- 在三个公共EEG数据集上训练并最初测试模型.
- 进一步测试了来自当地患者的手动注释的EEG数据集上的模型.
主要成果:
- 随机森林和卷积神经网络在公共数据集上表现最好.
- 在对局部患者数据进行测试时,特别是在神经网络方面,观察到显著的精度下降.
- 绩效差异表明,将模型推广到各种临床数据中存在挑战.
结论:
- 目前用于EEG发作检测的机器学习模型需要改进以获得临床应用.
- 重新训练的模型和新获得的数据可能会提高人工智能工具在诊断中的准确性.
- 弥合研究准确性和临床实用性之间的差距对于人工智能集成至关重要.
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