Time-frequency signal processing for seizure prediction in epileptic patients

目前有近30万加拿大人患有癫痫，每年诊断出约14，000例新病例。对于许多患者来说，癫痫发作是突然的，猛烈的，没有预警信号，可以通过药物或其他手段进行预防性干预。在精神健康的这一主要领域，只有当将各种科学、技术和医学专业结合起来的研究计划得到高度重视时，才能对活跃的大脑功能（或功能障碍）有更多的了解，就像神经科学的其他领域一样，如衰老。该研究项目提出了一种旨在预防癫痫发作的颅内EEG（脑电图）分析方法。目标是能够从EEG信号中提取和量化尽可能多的准瞬时信息，以检测癫痫发作的预警信号。我们的方法不是试图映射涉及数十亿个神经元在复杂网络中相互连接的基本过程，这些神经元无视任何详尽的分析，而是依赖于现代信号和信息处理工具，并实施从复杂系统科学中借鉴的方法和技术。评价使用真实的数据，这种软件嵌入的方法，可在互联网上的大扩散的研究界，旨在提供一个EEG信号处理平台，能够快速和精确的分析，使癫痫发作的早期预警。

Nearly 300,000 Canadians currently suffer from epilepsy and about 14,000 new cases are diagnosed each year. For many patients, the onset of seizures is sudden, violent and without forewarning signs to allow preventive intervention by either medicinal or other means. In this major area of mental health, increased understanding of active brain functioning (or malfunctioning) will be acquired only when research programs bringing together a variety of scientific, technological and medical specializations are given high priority, as is the case in other areas of neuroscience, such as aging. This research project proposes a methodology for intracranial EEG (electro encephalography) analysis aimed at seizure prevention. The goal is to be able to extract and quantify quasi-instantaneously as much information as possible from EEG signals to detect forewarning signs of seizures. Rather than attempting to map elementary processes involving billions of neurons interconnected in complex networks that defy any exhaustive analysis, our methodology relies on modern signal and information processing tools and implements approaches and techniques borrowed from the science of complex systems. Evaluated using real data, this software-embedded methodology, available on Internet for a large diffusion in the research community, aims to provide an EEG signal processing platform capable of quick and precise analysis allowing early warning of the onset of seizures.