Objective: High 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.
Methods: Intracranial 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.
Results: EMG 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.
Conclusions: Evaluation 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.
Keywords: Algorithms; EEG; Epilepsy; High Frequency Oscillations; Muscle artifacts.
Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.