Abstract
Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p<0.01). Fewer readers could lateralize seizure-onset (p<0.05). The confidence measures of the assignments were low (probable-unlikely), but increased using AR2 (p<0.05). The ICC for identifying the time of seizure-onset was 0.15 (95% confidence interval (CI), 0.11-0.18) using AR1 and 0.26 (95% CI 0.21-0.30) using AR2. The EEG interpretations were often consistent with behavioral, neurophysiological, and neuro-radiological findings, with left sided assignments correct in 95.9% (CI 85.7-98.9%, n=4) of cases using AR2, and 91.9% (77.0-97.5%) (n=4) of cases using AR1. Conclusions: EEG artifact reduction methods for localizing seizure-onset does not result in high rates of interpretability, reader confidence, and inter-reader agreement. However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.
Highlights
The scalp electroencephalogram (EEG) is a critical diagnostic tool in the evaluation of seizures, but artifact from muscle contraction often limits its use because of the obscuring of the cerebrally generated potentials
We determined if this maximum mutual information value exceeded a threshold value defined by visual inspection of the scalp EEG used in the experimental dataset, and if that electrode should be included in subsequent artifact reduction processing
In this study, we present a new artifact reduction software, AR2, and its application compared with a commercially available tool, AR1. 26 neurologists used the two methods to interpret 23 ictal EEG recordings that were uninterpretable due to muscle artifact when reviewed with conventional filtering
Summary
The scalp electroencephalogram (EEG) is a critical diagnostic tool in the evaluation of seizures, but artifact from muscle contraction often limits its use because of the obscuring of the cerebrally generated potentials. This problem is present in 11% of ictal EEGs overall and up to 70% of frontal lobe seizures[1,2,3]. The inability, or lack of precision, to discern the seizure-onset zone from scalp EEG often necessitates additional testing, including (positron emission tomography) PET, magnetoencephalography, ictal Single-photon emission computed tomography (SPECT), and intracranial EEG4 Each of these tests adds undesired time and cost to the evaluation. Ictal scalp EEG recordings present extraordinary challenges to ICA artifact reduction algorithms because the number of EMG artifact sources increases
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