Embedded Files
Translating epilepsy biomarkers
One of the most important goals in epilepsy research is to identify better biomarkers, both of acute changes and of disease progression. Using neural engineering techniques, a major goal of the lab is to characterize new biomarkers that indicate the timing and location of seizures, especially those seen in EEG signals. The most promising biomarker is currently High Frequency Oscillations (HFOs). HFOs have a clear relationship with epileptic networks, but it is still unclear how to implement them in a clinically-meaningful and safe fashion. The primary goal of this project is to develop and translate tools that will allow clinicians to utilize HFOs and improve clinical care.
Algorithm development
The most important limitation of HFO research is identifying them. Most clinical studies have relied upon manual review, which takes many hours and is not a viable clinical solution. Automated algorithms can be useful, but are very prone to identifying artifacts.
We developed an algorithm that removes artifacts and greatly improves precision. This algorithm runs in real time and has already identified over 5 million HFOs on 25 patients.
Seizure onset zone detection
After identifying HFOs, we developed an automated, prospective method of identifying regions of brain tissue with anomalously high HFO rates. This algorithm correctly identifies electrodes within the seizure onset zone in many patients, but importantly also avoids making predictions in patients for whom the rate is unreliable.
HFO features as an epilepsy biomarker
In addition to rate, HFOs have many other features that may be useful as an epilepsy biomarker. We have analyzed massive datasets of HFOs to identify relationships with seizure onset zone and time until the next seizure. This analysis is now moving forward with the algorithms above, which allow much more robust analysis.
In addition, we are analyzing HFO features to help distinguish between HFOs that are produced by normal brain rhythms versus epileptic tissue. This work involves using and developing cutting edge Big Data tools.
A unique aspect of our HFO work is the ability to analyze many hours-the entire hospital recording-for HFOs. This has provided us unique insights into the temporal and spatial characteristics of HFOs. Using this tool we found that the location of maximum HFOs can vary over time, much like different areas of the brain become active with seizures over time. We also found that HFOs change prior to seizure onset.
Page updated
Report abuse