Time-frequency signal processing for seizure prediction in epileptic patients

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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.