使用机器学习检测发作:系统审查和元分析
Lin Bai1, Gerhard Litscher1,2, Xiaoning Li3
1Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China.
Brain sciences
|June 26, 2025
概括
机器学习模型在使用电脑电图 (EEG) 信号检测发作方面表现出很高的准确性. 这一元分析证实了它们在早期诊断和治疗方面的潜力,尽管建议进一步进行临床验证.
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:
- 发作是不可预测的,严重影响患者的生活质量.
- 早期和准确的发作检测对于有效的管理至关重要.
- 机器学习 (ML) 提供使用电脑电图 (EEG) 信号的自动发作检测功能.
研究的目的:
- 进行元分析,评估ML模型用于发作检测的性能.
- 确定影响ML模型性能的因素,包括模型类型,数据预处理和数据集特征.
- 为开发智能发作检测工具提供基于证据的基础.
主要方法:
- 在多个数据库中进行系统的文献搜索,直到2025年4月.
- 包括60项研究和93个数据集用于元分析.
- 使用Stata 17.0.0计算聚合的灵敏度,特异性和AUC.
- 分析子组以调查异质性和出版偏见.
主要成果:
- ML模型实现了高的聚合性能:灵敏度为0.96,特异性为0.97,AUC为0.99.
- 观察到显著的异质性,受模型类型,数据预处理和数据集特征的影响.
- 这些发现表明ML在基于EEG的发作检测中表现强.
结论:
- 机器学习模型对基于EEG的自动发作检测有很大的前景.
- 将ML集成到成像设备中可以提高早期的诊断.
- 进一步的大规模,多中心的临床研究对于验证ML算法在现实世界中应用,可解释性和安全性至关重要.
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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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