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.

研究人员开发了灵活、主动、多路复用的记录设备，可以与植入大脑表面的数千个电极进行连接，同时这项技术可以更精细地观察大脑的电活动，并提供处理和分类的分析方法。目前缺乏对这些设备产生的大量数据的响应，许多现有的神经数据分析依赖于手动检查，而新的神经接口具有数千个通道，因此无法进行手动检查。自动化机器学习技术的微妙特征在这项研究中，研究人员开发了高效、灵敏的自动化方法来分析癫痫患者的微皮质电图 (μECoG) 数据，这些方法用于分割、分类和预测时空癫痫样放电（或尖峰）模式。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以及最终为癫痫患者提供更好的治疗的关键。这项研究包括四个相互关联的部分。第一个部分开发用于检测和预测的技术。隔离尖峰片段，并提取捕获每个尖峰的时空模式的特征。第二个组件开发无监督的聚类算法，可以根据精心选择的特征识别不同的尖峰运动模式簇。不仅根据其时空模式，而且根据过去尖峰的模式，将每个尖峰分为几个类别（发作间期、发作前、发作期和发作后）。预测尖峰波前位置的方法将能够预测癫痫发作和实时响应脑刺激以抑制癫痫发作。