Neural Network System for Detection of EEG Microsleeps

用于检测脑电图微睡眠的神经网络系统

• 批准号: 6338195
• 负责人: UDO TRUTSCHEL
• 金额: $ 9.35万
• 依托单位: CIRCADIAN TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-20 至 2002-04-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6338195
• 关键词: artificial intelligence; biomedical automation; computational neuroscience; computer data analysis; computer program /software; computer system design /evaluation; data collection methodology /evaluation; electroencephalography; electrophysiology; human data; neural information processing; sleep

DESCRIPTION (Verbatim from the Applicant's Abstract): A software system based on Artificial Neuro-fuzzy hybrid technology will be developed for automatic detection of microsleep events from EEG data. The software system will be designed for used as a model-free and rule-free classification tool that achieves generalization power through learning from examples. The development of the software system will require a Graphical User Interface for data example selection, frequency-analytic preprocessing of EEG raw data, feature extraction for microsleep characterization, design and training of neural networks for single EEG channels, and a fuzzy system for contextual combination of network response for multiple EEG channels to a single system response. The training and testing of the neural networks will be based on a database of visually scored examples of microsleep and non-microsleep events from electrophysiological data, which will be randomly divided into training, validation and test sets. The performnance of the software system will be evaluated based on the false-positive and false-negative rate for the microsleep detection using data examples unknown to the system. The agreement rate between the combined network response and results from visual and conventional automatic scoring will be used as additional evaluation parameter. PROPOSED COMMERCIAL APPLICATION: The software system will be an attractive tool for researchers, medical and technical personal, industrial engineers. It enables the user to quantify alertness/sleepiness in studies on sleep disorders, shiftwork, drug effects and fatigue countermeasures. It will help reduce time-consuming visual scoring by human experts. In addition, it will widen our knowledge about the rapid transition events (microsleeps) between wake and sleep and can contribute to the development of alertness monitor systems.

描述（逐字摘自申请人的摘要）：基于软件系统 人工神经模糊混合技术将被开发用于自动 从脑电图数据中检测微睡眠事件。软件系统将是 设计用作无模型和无规则的分类工具 通过从例子中学习获得泛化能力。 软件系统的开发需要图形用户界面 用于数据示例选择、脑电图原始数据的频率分析预处理， 用于微睡眠表征、设计和训练的特征提取 用于单个脑电图通道的神经网络，以及用于上下文的模糊系统 将多个 EEG 通道的网络响应组合到单个系统 回复。 神经网络的训练和测试将基于数据库 视觉评分的微睡眠和非微睡眠事件的例子 电生理数据，将被随机分为训练、 验证和测试集。 软件系统的性能评价将基于 使用数据进行微睡眠检测的假阳性和假阴性率 系统未知的示例。组合网络之间的一致率 视觉和传统自动评分的反应和结果将是 用作附加评估参数。 拟议的商业应用： 该软件系统对于研究人员、医疗和技术人员来说将是一个有吸引力的工具 个人、工业工程师。它使用户能够量化警觉性/睡意 从事睡眠障碍、轮班工作、药物效应和疲劳对策的研究。 它将有助于减少人类专家耗时的视觉评分。此外，它 将扩大我们对之间的快速过渡事件（微睡眠）的了解 唤醒和睡眠，可以有助于警觉性监测系统的开发。