CIF: Small: High Resolution EEG Signal Analysis for Seizure Detection and Treatment

CIF：小型：用于癫痫检测和治疗的高分辨率脑电图信号分析

• 批准号: 1422914
• 负责人: Yao Wang
• 金额: $ 49.47万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422914&HistoricalAwards=false
• 关键词: CIF; Small; Resolution; EEG; Signal

The investigators have developed flexible, active, multiplexed recording devices to enable interface with thousands of electrodes implanted on the surface of the brain. While this technology has enabled a much finer view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of data produced by these devices are presently lacking. Many existing neurological data analyses rely on manual inspection. With new neural interfaces with thousands of channels, the data volume is infeasible for manual review. Further, manual inspection can miss subtle features that automated machine learning techniques can detect. In this research, the investigators develop efficient and sensitive automated methods to analyze micro-electrocorticographic (µECoG) data from patients with epilepsy. These methods are used to segment, categorize and predict spatiotemporal epileptiform discharge (or spike) patterns. Understanding the ordering and relationships between these patterns is a key to developing better seizure detection and prediction techniques and ultimately better therapies for patients with epilepsy.This research comprises four interconnected components. The first component develops techniques for detecting and isolating spike segments, and for extracting features that capture the spatio-temporal pattern of each spike. The second component develops unsupervised clustering algorithms that can identify distinct clusters of spike motion patterns based on carefully chosen features. The thir-d component develops classifiers that can categorize each spike into a few classes (inter-ical, pre-ictal, ictal and post-ictal) based on not only its spatio-temporal pattern, but also the patterns of past spikes. The final component develops methods to predict spike wavefront locations. The combination of these methods will enable seizure prediction and real-time responsive brain stimulation to suppress seizures.

研究人员开发了柔性，活跃，多路复用的记录设备，以使与植入大脑表面的数千个电子的接口。尽管这项技术使人们对大脑的电活动有了更好的看法，但目前缺乏处理，对这些设备产生的大量数据进行处理，分类和响应的分析方法。许多现有的神经数据分析依赖于手动检查。使用具有数千个渠道的新神经元接口，数据量不可行用于手动审查。此外，手动检查可能会错过自动化机器学习技术可以检测到的微妙功能。在这项研究中，研究人员开发了有效且敏感的自动化方法，以分析癫痫患者的微皮质学（µECOG）数据。这些方法用于细分，分类和预测空间临时癫痫样放电（或尖峰）模式。了解这些模式之间的顺序和关系是开发更好的癫痫发作检测和预测技术的关键，并最终为癫痫患者提供更好的疗法。这项研究包括四个相互联系的组件。用于检测和隔离尖峰片段的第一个组件开发技术，以及提取捕获每个峰值的空间周期图案的特征。第二个组件会开发无监督的聚类算法，这些聚类算法可以识别基于精心选择的特征的尖峰运动模式的独特的峰值簇。 THIR-D组件开发人员可以根据其时空模式，而且还基于过去尖峰的模式，将每个尖峰分为几个类别（互际，前，ICTAL和ictictal）的几个类别（互际，前，ICTAL和ICTAL）。预测尖峰波前位置的最终组件开发方法。这些方法的组合将使癫痫发作预测和实时反应式脑刺激抑制癫痫发作。