DEFINITION AND DETECTION OF CLINICAL EEG FEATURES

临床脑电图特征的定义和检测

• 批准号: 6505501
• 负责人: GEORGIY R MINASYAN
• 金额: $ 30.65万
• 依托单位: ASTRO-MED, INC.
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6505501
• 关键词: biomedical equipment development; computer program /software; electroencephalography

DESCRIPTION (Adapted from Applicant's Abstract): This Phase I project will produce PC-base software capable of detecting first level feature (FLF) of clinical utility, such as spikes, K-complexes, and artifacts , from routine EEG's,long-term monitoring , and sleep studies. The goal following Phase II sill be software capable of identifying third level features (TLF), corresponding to clinical diagnostic features. In Phase I, a panel of clinical experts will define FLF and score recordings from our available database. The database will be split into training and tests sets. EEG time domain and frequency domain parameters from the training set will be calculated and entered into the first layer of an artificial neural network (ANN). Automatic classification of FLF in the test set will be compared to expert scoring. A successful completion of Phase I will be 90 percent correct automatic. During Phase II, FLF will be entered into a second level ANN. The resulting second level features (SLF) will be compared with expert classification of patient states, such as sleep, arousal, and seizures. FLF and SLF will then be combined into TLF using syntactical analysis and adaptive segmentation to match expert clinical classifications. TLF represent clinical diagnostic features, such as a focal lesion, epileptiform, or fragmented sleep. PROPOSED COMMERCIAL APPLICATION: Not available.

描述（改编自申请人的摘要）：此阶段I项目 将产生能够检测第一级功能的PC基本软件 （FLF）临床实用程序，例如尖峰，k复合物和工件 ，从常规的脑电图，长期监测和睡眠研究。 目标 遵循II阶段的SILL是能够识别第三级的软件 功能（TLF），对应于临床诊断特征。 在第一阶段，临床专家小组将定义FLF并得分 来自我们可用数据库的录音。 数据库将分为 培训和测试集。 脑电图时间域和频域 训练集的参数将被计算并输入 人工神经网络（ANN）的第一层。 自动的 测试集中FLF的分类将与专家进行比较 得分。 成功完成第一阶段将是90％正确的 自动的。 在第二阶段，FLF将进入第二级ANN。 这 将最终的第二级功能（SLF）与专家进行比较 患者状态的分类，例如睡眠，唤醒和癫痫发作。 然后，使用句法分析将FLF和SLF合并为TLF 和自适应细分以匹配专家临床分类。 TLF 代表临床诊断特征，例如局灶性病变， 癫痫样或零散的睡眠。 拟议的商业应用：不可用。