基于脑电图 (EEG) 的诊断通过使用共享的隐藏空间驱动多视图学习的多功能空间融合.
Xiujian Hu1, Yicheng Xie1, Hui Zhao1
1Department of Electronics and Information Engineering, Bozhou University, Bozhou, Anhui, China.
PeerJ. Computer science
|March 14, 2024
概括
一个新的多视图学习算法可以从脑电图 (EEG) 数据中改进的识别. 通过创建共享的隐藏空间,它提高了功能利用率,比单视图方法更高的准确性.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 是一种常见的神经疾病,使用电脑电图 (EEG) 进行诊断.
- 多视图学习 (MVL) 有助于从复杂的EEG特征中自动识别.
- 现有的MVL方法在面试样本差异方面遇到了困难.
研究的目的:
- 提出一种新的共享隐藏空间驱动的多视图学习算法,用于的识别.
- 为了解决目前用于EEG分析的MVL方法中的挑战.
- 为了提高自动诊断的准确性和稳定性.
主要方法:
- 开发了一个共享的隐藏空间驱动的多视图学习算法.
- 利用内核密度估计来构建一个共享的隐藏空间.
- 结合原创和共享空间,创造了一个扩展的学习空间.
主要成果:
- 拟议的算法在波恩大学病数据集上显示出有前途的性能.
- 实现了0.9787.7的平均分类准确度.
- 在分类准确度方面,单视图方法的性能至少超过4%.
结论:
- 共享的隐藏空间驱动的MVL算法有效地提高了基于EEG的的识别.
- 该方法充分利用了不同数据视图中的相关信息.
- 这种方法在自动诊断方面取得了重大进展.
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Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types: