Improving the Interpretation of Ictal Scalp EEG: BSS–CCA Algorithm for Muscle Artifact Removal

Abstract

To investigate the potential clinical relevance of a new algorithm to remove muscle artifacts in ictal scalp EEG. Thirty-seven patients with refractory partial epilepsy with a well-defined seizure onset zone based on full presurgical evaluation, including SISCOM but excluding ictal EEG findings, were included. One ictal EEG of each patient was presented to a clinical neurophysiologist who was blinded to all other data. Ictal EEGs were first rated after band-pass filtering, then after elimination of muscle artifacts using a blind source separation-canonical correlation analysis technique (BSS-CCA). Degree of muscle artifact contamination, lateralization, localization, time and pattern of ictal EEG onset were compared between the two readings and validated against the other localizing information. Muscle artifacts contaminated 97% of ictal EEGs, and interfered with the interpretation in 76%, more often in extratemporal than temporal lobe seizures. BSS-CCA significantly improved the sensitivity to localize the seizure onset from 62% to 81%, and performed best in ictal EEGs with moderate to severe muscle artifact contamination. In a significant number of the contaminated EEGs, BSS-CCA also led to an earlier identification of ictal EEG changes, and recognition of ictal EEG patterns that were hidden by muscle artifact. Muscle artifacts interfered with the interpretation in a majority of ictal EEGs. BSS-CCA reliably removed these muscle artifacts in a user-friendly manner. BSS-CCA may have an important place in the interpretation of ictal EEGs during presurgical evaluation of patients with refractory partial epilepsy.