AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

• Published in: F1000 research 2017, Vol.6, p.30-30
• Main Authors: Weiss, Shennan Aibel, Asadi-Pooya, Ali A, Vangala, Sitaram, Moy, Stephanie, Wyeth, Dale H, Orosz, Iren, Gibbs, Michael, Schrader, Lara, Lerner, Jason, Cheng, Christopher K, Chang, Edward, Rajaraman, Rajsekar, Keselman, Inna, Churchman, Perdro, Bower-Baca, Christine, Numis, Adam L, Ho, Michael G, Rao, Lekha, Bhat, Annapoorna, Suski, Joanna, Asadollahi, Marjan, Ambrose, Timothy, Fernandez, Andres, Nei, Maromi, Skidmore, Christopher, Mintzer, Scott, Eliashiv, Dawn S, Mathern, Gary W, Nuwer, Marc R, Sperling, Michael, Engel, Jr, Jerome, Stern, John M
• Format: Article
• Language: English
• Subjects: Neuroimaging; Software Tool
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.10569.2

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. 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. 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