Department of Neurology, University of Michigan, USA.
Department of Neurology, University of Michigan, USA; Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, USA.
Clin Neurophysiol. 2019 Jun;130(6):976-985. doi: 10.1016/j.clinph.2019.03.028. Epub 2019 Apr 11.
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.
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.
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.
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.
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.
高频振荡(HFOs)是一种有前途的癫痫生物标志物。HFOs 通常是在颅内电极上获得的,但在 HFO 分析中,肌肉伪影的污染仍然是一个问题。本文评估了肌源性伪影对颅内 HFO 检测的影响以及如何去除这些伪影。
对 31 名患者进行了颅内 EEG 记录。使用自动算法对整个记录进行 HFO 检测。当有条件时,同时使用头皮 EEG 来识别肌肉伪影的时期。这些标记用于训练自动头皮 EMG 检测器和颅内 EMG 检测器。通过与术后结果良好的患者的致痫区和切除体积进行比较,评估其对癫痫组织的特异性。
EMG 伪影很频繁,会产生大量的假 HFO,尤其是在前颞叶。头皮和颅内 EMG 检测器都具有很高的准确性。去除假 HFO 可提高对癫痫组织的特异性。
HFO 的评估需要考虑肌肉伪影的影响。我们提出了两种工具,可以有效地减轻肌肉伪影对 HFO 的影响。
去除肌肉伪影可提高 HFO 对癫痫组织的特异性。未来的 HFO 研究应考虑到这一影响,尤其是在使用自动化算法或头皮电极不存在的情况下。