The intrinsic value of HFO features as a biomarker of epileptic activity
This work addresses challenges in analyzing discrete events in EEG data for epilepsy diagnosis, but it is incremental as it builds on existing HFO biomarker research.
The study tackled the problem of analyzing high frequency oscillations (HFOs) as biomarkers for epileptic activity by assessing the appropriateness of linear dimensionality reduction methods and the consistency of manifolds across time, space, and patients, and estimated bounds on Bayes classification error to quantify the distinction between seizure-related and other HFOs, providing a foundation for clinical use.
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution, intracranial EEG data. Two primary challenges are 1) dimensionality reduction, and 2) assessing feasibility of classification. Dimensionality reduction assumes that the data lie on a manifold with dimension less than that of the feature space. However, previous HFO analyses have assumed a linear manifold, global across time, space (i.e. recording electrode/channel), and individual patients. Instead, we assess both a) whether linear methods are appropriate and b) the consistency of the manifold across time, space, and patients. We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs (those occurring during seizures and those occurring due to other processes). This analysis provides the foundation for future clinical use of HFO features and buides the analysis for other discrete events, such as individual action potentials or multi-unit activity.