对信号处理和机器学习技术进行审查,以检测间接性形放电
Bahman Abdi-Sargezeh1, Sepehr Shirani2, Saeid Sanei2
1Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; School of Science and Technology, Nottingham Trent University, Nottingham, UK.
Computers in biology and medicine
|December 9, 2023
概括
使用机器学习检测间接性发泄物 (IED) 对管理至关重要. 本综述对EEG数据的各种IED检测方法进行了分类和比较,突出了它们的优缺点.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 间接性发性排泄 (IED) 是的关键指标,发生在发作之间.
- 准确的IED检测对于临床治疗和患者的管理至关重要.
- 自1972年以来,机器学习技术在自动化IED检测方面已经显著发展.
研究的目的:
- 从脑电图 (EEG) 数据中检测IED的现有机器学习方法的审查和分类.
- 为了比较不同IED检测方法的性能,优势和局限性.
- 确定IED自动检测当前的挑战和未来的研究方向.
主要方法:
- 将IED检测技术分为六组:模板匹配,特征表示,矩阵分解,张量分解,神经网络和从scEEG进行iEEG估计.
- 基于准确度,灵敏度和特异性等指标的方法的定量比较.
- 对每个方法的一般优势和局限性的定性分析.
主要成果:
- 内EEG (iEEG) 信号为IED识别提供了较高的细节,相比头皮EEG (scEEG),由于信号衰减较低.
- 包括深度学习在内的各种机器学习方法对自动化IED检测充满希望.
- 提供了IED检测技术的最新状态的全面概述.
结论:
- 强大的IED检测方法对于推进诊断和患者护理至关重要.
- 需要进一步的研究来克服现有的自动化IED检测的局限性,特别是使用scEEG数据.
- 该综述为领域的研究人员和临床医生提供了宝贵的资源.
关键词:
深度神经网络是一种深度神经网络.这是一个EEGEEGEEGEEGEEGEEGEEG.是一种病.这是一个IED.模仿的特征是模仿的特征.模板匹配的匹配方式张量因子分解的使用.scEEG到iEEG的翻译更多相关视频
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