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

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...

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Bibliographic Details
Published in:Epilepsia (Copenhagen) Vol. 61; no. 8; pp. 1553 - 1569
Main Authors: Remakanthakurup Sindhu, Kavyakantha, Staba, Richard, Lopour, Beth A.
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.08.2020
Subjects:
Algorithms
Automation
biomarker
Biomarkers
Brain - physiopathology
Brain Mapping
Brain Waves
Convulsions & seizures
EEG
Electrocorticography - trends
Electroencephalography - trends
Epilepsy
Epilepsy - diagnosis
Epilepsy - physiopathology
epileptogenic zone
fast ripple
Humans
Oscillations
Reproducibility of Results
Reviews
ripple
seizure localization
seizure‐onset zone
Signal Processing, Computer-Assisted
Surgery
Trends
epileptogenic zone
fast ripple
ripple
seizure localization
biomarker
seizure-onset zone
ISSN:0013-9580, 1528-1167, 1528-1167
Online Access:Get full text
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Summary: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.
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ISSN:0013-9580
1528-1167
1528-1167
DOI:10.1111/epi.16622