通过深度学习和不确定性估计来推进EEG预测
Mats Tveter1,2, Thomas Tveitstøl3,4, Christoffer Hatlestad-Hall3
1Department of Neurology, Oslo University Hospital, Oslo, Norway. matstv@ous-hf.no.
Brain informatics
|October 27, 2024
概括
深度学习模型可以从脑电图 (EEG) 数据中高精度地预测性别. 结合不确定性估计和深度合集可以提高这些医疗保健应用程序的可靠性和性能.
科学领域:
- 神经科学和人工智能 人工智能
- 计算神经科学是一种神经科学.
- 医疗保健中的机器学习
背景情况:
- 深度学习 (DL) 在医疗保健中为疾病检测和诊断提供了潜力.
- 缺乏可解释性和复杂性阻碍了在关键医疗预测中采用DL.
- 不确定性估计和可解释性措施对于建立对DL系统的信任至关重要.
研究的目的:
- 作为一个基准,研究DL模型从脑电图 (EEG) 数据预测性别.
- 探索DL乐团的使用,以提高性能和可解释性.
- 评估不确定性估计在提高DL模型可靠性和性能方面的作用.
主要方法:
- 利用InceptionNetwork和EEGNet模型从EEG数据中预测性别.
- 采用DL组合,结合模型变异,以提高预测准确度.
- 实施了五重交叉验证,以进行可靠的绩效评估.
- 使用数据驱动方法分析了频段与性别预测之间的关系.
主要成果:
- 一个单一的InceptionNetwork模型实现了90.7%的准确性和0.947.7的AUC.
- 最好的DL组合在EEG数据的性别预测中达到91.1%的准确性.
- 通过深层集团进行不确定性估计,提高了预测性能.
- 模型成功地分类了所有频段的性别,揭示了性别特征.
结论:
- 对于EEG数据分析,DL模型,特别是与不确定性估计相结合的模型,显示出有前途.
- 来自EEG的性别预测是开发医疗保健中可解释和可信赖的AI的宝贵基准.
- 性别特定的神经特征存在于所有EEG频段.
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