ReviewBiomarkers in medicine2019
Localizing epileptogenic regions using high-frequency oscillations and machine learning.
Review in Biomarkers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.Scientific reports · 2026Article
- Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning Framework.Research square · 2026Article
- Spikes on ripples are better interictal biomarkers of epilepsy than spikes or ripples.Brain communications · 2025Article
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.Epilepsia open · 2024Article
- Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy.Neuroscience bulletin · 2024Review
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Capturing the power of seizures: an empirical mode decomposition analysis of epileptic activity in the mouse hippocampus.Frontiers in molecular neuroscience · 2023Article
- An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.Epilepsia open · 2022Article
- Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone.Journal of neural engineering · 2022Article
- Epileptogenic high-frequency oscillations present larger amplitude both in mesial temporal and neocortical regions.Frontiers in human neuroscience · 2022Article
- Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients.Frontiers in molecular biosciences · 2022Article
- Three dimensional microelectrodes enable high signal and spatial resolution for neural seizure recordings in brain slices and freely behaving animals.Scientific reports · 2021Article
- Epileptic High-Frequency Oscillations in Intracranial EEG Are Not Confounded by Cognitive Tasks.Frontiers in human neuroscience · 2021Article
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- Double-Step Machine Learning Based Procedure for HFOs Detection and Classification.Brain sciences · 2020Article
- HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy.Epilepsy currentsArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.
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Registered trials
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.