ArticleCommunications medicine2024
Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Implant-Grade Neural Sensing with Artifact-Resilient Computational Intelligence Preserves Spatial Fidelity of HFO Biomarkers in Epilepsy.Research square · 2026Article
- Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study.Journal of medical Internet research · 2026Article
- Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte.Journal of computational neuroscience · 2026Article
- Multi-montage visualization of interictal epileptic events in high-density intraoperative electrocorticography.Clinical neurophysiology practice · 2026Article
- Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.Journal of neural engineering · 2025Review
- Spurious Spike Elimination using Sparse Signal Processing Improves Seizure Onset Zone Delineation in Brief Intraoperative iEEG Recordings.The ... Midwest Symposium on Circuits and Systems conference proceedings : MWSCAS. Midwest Symposium on Circuits and Systems · 2025Article
- Integrating Spatial-Temporal Channel Reliability with AI for Robust High-Frequency Oscillation Detection in Intracranial EEG.International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering · 2025Article
- Data-Driven Hyperparameter Tuning of High-Frequency Oscillation Detection for Seizure-Onset Zone Localization.International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
backgroundWhile high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.
methodsIn response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery.
resultsOur framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ.
conclusionsThese findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy.
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Registered trials
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