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.

研究人员已经开发出灵活、主动、多路复用的记录设备，能够与植入大脑表面的数千个电极连接。虽然这项技术能够更好地观察大脑的电活动，但目前缺乏处理、分类和响应这些设备产生的大量数据的分析方法。许多现有的神经学数据分析依赖于人工检查。使用具有数千个通道的新神经接口，数据量对于手动审查是不可行的。此外，手动检查可能会错过自动机器学习技术可以检测到的细微特征。在这项研究中，研究人员开发了有效和灵敏的自动化方法来分析癫痫患者的微皮层脑电图（µECoG）数据。这些方法被用来分割，分类和预测时空癫痫样放电（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以及最终为癫痫患者提供更好治疗的关键。第一个组成部分开发技术，用于检测和隔离尖峰段，并提取功能，捕捉每个尖峰的时空模式。 第二部分开发了无监督聚类算法，可以根据精心选择的功能识别不同的簇的尖峰运动模式。第三-d组件开发分类器，可以将每个尖峰分为几个类（inter-ical，pre-ictal，ictal和post-ictal），不仅基于其时空模式，而且基于过去尖峰的模式。最后一部分开发的方法来预测尖峰波阵面的位置。这些方法的组合将使癫痫发作预测和实时响应脑刺激能够抑制癫痫发作。