ArticleBrain communications2022
Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.
Article in Brain communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management.Frontiers in neurology · 2025Pooled it
- Common Data Elements for Preclinical High-Frequency Oscillations in Epilepsy.Epilepsy currents · 2026Review
- Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Zebrafish models of developmental epileptic encephalopathy accurately reflect clinical electrographic biomarkers.Epilepsia · 2026Article
- Phenotypic Classification of Scalp High-Frequency Oscillations in Absence Epilepsy Based on Multiple Characteristics Using K-Means Clustering.Bioengineering (Basel, Switzerland) · 2026Article
- Localizing the epileptogenic zone in drug-resistant epilepsy based on ictal SEEG high-resolution time-frequency representation.Frontiers in neuroscience · 2026Article
- Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy.Epilepsia · 2025Article
- PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.Journal of neural engineering · 2025Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- Review
- Frontiers in EEG as a tool for the management of pediatric epilepsy: Past, present, and future.Epilepsia open · 2025Article
- Fast ripple band high-frequency activity associated with thalamic sleep spindles in pediatric epilepsy.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- SEEG in 2025: progress and pending challenges in stereotaxy methods, biomarkers and radiofrequency thermocoagulation.Current opinion in neurology · 2025Review
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy.Frontiers in neurology · 2025Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.Journal of neural engineering · 2024Article
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- 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
- Characterizing physiological high-frequency oscillations using deep learning.Journal of neural engineering · 2022Article
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
Abstract
Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The 'purification power' of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%,
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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.