Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.

Kavyakantha Remakanthakurup Sindhu, Richard Staba, Beth A Lopour
Author Information
  1. Kavyakantha Remakanthakurup Sindhu: Biomedical Engineering, UC Irvine, Irvine, California, USA. ORCID
  2. Richard Staba: Neurology, UCLA, Los Angeles, California, USA.
  3. Beth A Lopour: Biomedical Engineering, UC Irvine, Irvine, California, USA. ORCID

Abstract

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

Keywords

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Grants

  1. U01 NS123128/NINDS NIH HHS
  2. R01 NS33310/NINDS NIH HHS
  3. U54 NS100064/NINDS NIH HHS
  4. R01 NS106957/NINDS NIH HHS
  5. R01 NS084017/NINDS NIH HHS
  6. RF1 NS033310/NINDS NIH HHS
  7. DOD EP180003/U.S. Department of Defense
  8. R01 NS033310/NINDS NIH HHS

MeSH Term

Algorithms
Artifacts
Brain
Brain Mapping
Brain Waves
Electrocorticography
Electroencephalography
Epilepsy
Humans
Reproducibility of Results
Signal Processing, Computer-Assisted