Sijin Ren1, Stephen V Gliske2, David Brang3, William C Stacey4. 1. Department of Neurology, University of Michigan, USA. Electronic address: sijinren@umich.edu. 2. Department of Neurology, University of Michigan, USA; Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, USA. Electronic address: sgliske@umich.edu. 3. Department of Psychology, University of Michigan, USA. Electronic address: djbrang@umich.edu. 4. Department of Neurology, University of Michigan, USA; Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, USA. Electronic address: william.stacey@umich.edu.
Abstract
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.
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.
Authors: Benoît Crépon; Vincent Navarro; Dominique Hasboun; Stéphane Clemenceau; Jacques Martinerie; Michel Baulac; Claude Adam; Michel Le Van Quyen Journal: Brain Date: 2009-11-17 Impact factor: 13.501
Authors: Richard J Staba; Leonardo Frighetto; Eric J Behnke; Gary W Mathern; Tony Fields; Anatol Bragin; Jennifer Ogren; Itzhak Fried; Charles L Wilson; Jerome Engel Journal: Epilepsia Date: 2007-07-28 Impact factor: 5.864
Authors: Greg A Worrell; Andrew B Gardner; S Matt Stead; Sanqing Hu; Steve Goerss; Gregory J Cascino; Fredric B Meyer; Richard Marsh; Brian Litt Journal: Brain Date: 2008-02-07 Impact factor: 13.501
Authors: Premysl Jiruska; Jozsef Csicsvari; Andrew D Powell; John E Fox; Wei-Chih Chang; Martin Vreugdenhil; Xiaoli Li; Milan Palus; Alejandro F Bujan; Richard W Dearden; John G R Jefferys Journal: J Neurosci Date: 2010-04-21 Impact factor: 6.167
Authors: Aljoscha Thomschewski; Nathalie Gerner; Patrick B Langthaler; Eugen Trinka; Arne C Bathke; Jürgen Fell; Yvonne Höller Journal: Front Neurol Date: 2020-10-19 Impact factor: 4.003
Authors: Jessica K Nadalin; Uri T Eden; Xue Han; R Mark Richardson; Catherine J Chu; Mark A Kramer Journal: J Neurosci Methods Date: 2021-06-04 Impact factor: 2.987