ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2019
Redaction of false high frequency oscillations due to muscle artifact improves specificity to epileptic tissue.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 20 citations in OpenAlex.
- Implant-Grade Neural Sensing with Artifact-Resilient Computational Intelligence Preserves Spatial Fidelity of HFO Biomarkers in Epilepsy.Research square · 2026Article
- The Influence of Recording Duration and Vigilance State on High-Frequency Oscillation Characterization in Epilepsy.Neurology · 2026Article
- Multicentre analysis of seizure outcome predicted by removal of high-frequency oscillations.Brain : a journal of neurology · 2025Observational
- High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery.Brain communications · 2024Article
- Macroscopic resting-state brain dynamics are best described by linear models.Nature biomedical engineering · 2024Article
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization.Journal of neural engineering · 2022Article
- Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram.Journal of neuroscience methods · 2021Article
- Interictal high frequency background activity as a biomarker of epileptogenic tissue.Brain communications · 2021Article
- Article
- Distinguishing false and true positive detections of high frequency oscillations.Journal of neural engineering · 2020Article
- Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.Epilepsia · 2020Review
- Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe.Frontiers in neurology · 2020Article
- Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction.Frontiers in human neuroscience · 2020Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveHigh Frequency Oscillations (HFOs) are a promising biomarker of epilepsy. HFOs are typically acquired on intracranial electrodes, but contamination from muscle artifacts is still problematic in HFO analysis. This paper evaluates the effect of myogenic artifacts on intracranial HFO detection and how to remove them.
methodsIntracranial EEG was recorded in 31 patients. HFOs were detected for the entire recording using an automated algorithm. When available, simultaneous scalp EEG was used to identify periods of muscle artifact. Those markings were used to train an automated scalp EMG detector and an intracranial EMG detector. Specificity to epileptic tissue was evaluated by comparison with seizure onset zone and resected volume in patients with good outcome.
resultsEMG artifacts are frequent and produce large numbers of false HFOs, especially in the anterior temporal lobe. The scalp and intracranial EMG detectors both had high accuracy. Removing false HFOs improved specificity to epileptic tissue.
conclusionsEvaluation of HFOs requires accounting for the effect of muscle artifact. We present two tools that effectively mitigate the effect of muscle artifact on HFOs. SIGNIFICANCE: Removing muscle artifacts improves the specificity of HFOs to epileptic tissue. Future HFO work should account for this effect, especially when using automated algorithms or when scalp electrodes are not present.
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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.