ReviewEpilepsia2020
Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.
Review in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Bridging computational and clinical strategies for presurgical identification of epileptogenic networks.Epilepsia open · 2026Article
- Morphological Remodeling of Scalp High-Frequency Oscillations Across BASED-Stratified Groups in Infantile Epileptic Spasms Syndrome.Diagnostics (Basel, Switzerland) · 2026Article
- Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Inroads into epilepsy through high-frequency oscillations: Achievements and benchmark areas for improvement.Epilepsia · 2026Review
- Consensus-based Sharp-Wave Ripple detection and its application in an alcohol administration model.Frontiers in systems neuroscience · 2026Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- Flexible Deep-Brain Probe for High-Fidelity Multi-Scale Recording of Epileptic Network Dynamics.Micromachines · 2025Article
- Association Between Scalp High-Frequency Oscillations and Burden of Amplitudes and Epileptiform Discharges (BASED) Scores in Infantile Epileptic Spasms Syndrome.Biomolecules · 2025Article
- Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework.IEEE journal of biomedical and health informatics · 2025Article
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- Electrode Surface Area Impacts Measurement of High Frequency Oscillations in Human Intracranial EEG.IEEE transactions on bio-medical engineering · 2024Article
- Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.Nature communications · 2024Article
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- SEEG4D: a tool for 4D visualization of stereoelectroencephalography data.Frontiers in neuroinformatics · 2024Article
- Passive and active markers of cortical excitability in epilepsy.Epilepsia · 2023Review
- Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2023Article
- Quantitative approaches to guide epilepsy surgery from intracranial EEG.Brain : a journal of neurology · 2023Article
- Epileptic Tissue Localization through Skewness-Based Functional Connectivity in the High-Frequency Band of Intracranial EEG.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.