使用可解释的人工智能,以获得基于脑电图信号的有效发作检测模型
Jusciaane Chacon Vieira1, Luiz Affonso Guedes1, Mailson Ribeiro Santos1
1Department of Computer Engineering and Automation-DCA, Federal University of Rio Grande do Norte-UFRN, Natal 59078-900, RN, Brazil.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一种简化方法,用于使用电脑电图 (EEG) 信号检测发作. 该方法以更少的特征和道实现了超过95%的准确性,使移动发作检测成为可行的.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 影响全球5000万,导致各种表现的发作.
- 发作严重影响生活质量,导致社会隔离和痛苦.
- 目前的检测方法通常依赖于复杂的机器学习或对EEG信号的深度学习.
研究的目的:
- 开发一种简化,可解释的人工智能 (XAI) 方法来检测发作.
- 为了减少精确发作检测所需的特征和EEG通道的数量.
- 在没有深度学习的情况下验证更简单的发作检测模型的有效性.
主要方法:
- 利用可解释的人工智能 (XAI) 来检测发作.
- 为更简单的分类器实施了特征和道减少策略.
- 在1秒的时间窗口内对EEG信号进行时间域分析.
主要成果:
- 在准确性,精度,回忆和F1分数方面实现了超过95%的性能指标.
- 仅使用六个特征和五个EEG通道,成功检测了发作.
- 在多样化的患者群体中表现出强大的概括性.
结论:
- 在更简单的模型中,功能减少足以有效检测发作.
- 电极的战略选择和减少的属性可以支持有效的移动发作检测应用程序.
- 拟议的XAI方法提供了一个有希望的,不那么复杂的检测替代方案.
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Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types: