Epilepsy is one of the world's most prevalent diseases, yet the rate of uncontrolled seizures has not changed in decades. One of the reasons for this is our limited understanding of seizure mechanisms, and so one of the main goals of epilepsy research is to identify new biomarkers to help us understand the nature of the disease. Recent technological advancements now allow us to monitor brain activity with much higher resolution, which have led to the identification of promising potential biomarkers such as High Frequency Oscillations (HFOs). Unfortunately, clinicians still have not determined how to utilize this information under clinical conditions. There are three main obstacles to implementing HFOs in practice: 1) it is unclear how to acquire them in a practical way; 2) it is unclear how to ascertain which HFOs are truly related to epilepsy; and 3) it is unclear how to use the HFO data in a prospective fashion to improve clinical care. The purpose of this project is overcome each of these obstacles.
The first Aim v alidates a universal computer algorithm that can identify HFOs automatically, then tests how to use HFO rate as method to identify where seizures will start. This method improves upon past work by improving the precision of HFO detection and determining how to avoid false predictions that would lead to unnecessary surgery.
The second Aim addresses a major unsolved problem in HFO research: HFOs are seen in normal brain as well as in epilepsy.
This Aim will use state-of-the- art machine learning tools to process a vast dataset of HFO collected from over 100 patients to determine how to distinguish epileptic from normal HFOs.
The third Aim will analyze how HFOs change over time, a largely unexplored characteristic of HFOs that cannot be evaluated without very large datasets.
These Aims together serve as the framework to establish HFOs as a clinically viable biomarker of seizures, allowing their translation into clinical epilepsy care and leading to future prospective clinical studies identifying the location and timing of seizure onset.
The goal of this project is to characterize a novel biomarker of seizures using advanced computer algorithms that monitor brainwaves in real time. These biomarkers, known as High Frequency Oscillations, have been recognized for some time but their research has been restricted to very short datasets within a handful of centers worldwide. This project will use Big Data tools to help translate these biomarkers into widespread use while exploring several novel ways in which they will help clinicians identify seizures.
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