REENTRANT NETWORK CLASSIFICATION OF EEG IN DEMENTIA

我们提出了一种先进的神经网络技术，用于识别 痴呆症的异常脑电活动。数字化脑电图 （脑电图）研究已经成功地区分了 阿尔茨海默氏症从多梗死性痴呆，使用相干数据从一个 常规19导EEG。我们公司最近的硬件进步 使密集传感器阵列（64至256通道）EEG便宜且方便 用于常规临床评价。然而，有效的临床应用 计量的进步需要分类的重大创新 方法论以前的EEG分类研究已经应用于- 传播或自组织映射网络。这些架构都在 固有地限制了它们表征动态特性的能力 多通道时间序列数据，包括EEG相干性。为了 克服这个限制，我们建议应用嵌套的可重入， 循环亥姆霍兹机最近在我们的实验室开发。的 该网络的动态实现了一个复杂的高维卡尔曼 过滤器，提取参数的最小描述长度（MDL）模型 定义时间序列数据。在第一阶段的项目中，EEG 亥姆霍兹机器架构的分类性能将是 与标准反向传播网络的性能相比。两 时间序列的措施和多元相干性是用来减少 在分类之前的密集阵列EEG的维度。 拟定商业应用： 作为药物和其他治疗痴呆症提供显着的改善 在功能方面，一种廉价的，可重复的神经系统评估方法， 功能可以提供每个治疗效果的定量数据， 病人一个简短的记忆测试，伴随着一个快速，舒适的脑电图， 可以成为衡量神经健康的标准， 对每一位老年人进行有效的医疗管理。

We propose an advanced neural network technology for recognizing the abnormal brain electrical activity of dementia. Digital EEG (electroencephalographic) studies have successfully differentiated Alzheimer's from multi-infarct dementia, using coherence data from a convential 19-channel EEG. Recent hardware advances by our company have made dense sensor array (64 to 256 channel) EEG inexpensive and convenient for routine clinical evaluation. However, effective clinical use of this advance in measurement requires a significant innovation in classification methodology. Previous classification research with EEG has applied back- propagation or self-organizing map networks. These architectures are inherently limited in their ability to characterize the dynamic properties of multi-channel time-series data, including EEG coherence. In order to surmount this limitation, we propose to apply the nested reentrant and recurrent Helmholtz machine recently developed in our laboratory. The dynamics of this network implement a complex high-dimensional Kalman filter, extracting a minimum-description-length (MDL) model of parameters defining the time-series data. In this Phase I project, the EEG classification performance of the Helmholtz machine architecture will be compared with the performance of a standard back-propagation network. Both time series measures and multivariate coherence are used to reduce the dimensionality of the dense array EEG prior to classification. PROPOSED COMMERCIAL APPLICATION: As drug and other therapies for dementia provide significant improvements in functioning, an inexpensive, repeatable assessment of neurological function could provide quantitative data on therapeutic efficacy in each patient. A brief memory test, accompanied by a quick, comfortable EEG, could become a criterion measure of neurological health to guide the effective medical management of each aged person.