1. AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software [version 2; referees: 2 approved]
- Author
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Shennan Aibel Weiss, Ali A Asadi-Pooya, Sitaram Vangala, Stephanie Moy, Dale H Wyeth, Iren Orosz, Michael Gibbs, Lara Schrader, Jason Lerner, Christopher K Cheng, Edward Chang, Rajsekar Rajaraman, Inna Keselman, Perdro Churchman, Christine Bower-Baca, Adam L Numis, Michael G Ho, Lekha Rao, Annapoorna Bhat, Joanna Suski, Marjan Asadollahi, Timothy Ambrose, Andres Fernandez, Maromi Nei, Christopher Skidmore, Scott Mintzer, Dawn S Eliashiv, Gary W Mathern, Marc R Nuwer, Michael Sperling, Jerome Engel Jr, and John M Stern
- Subjects
Neuroimaging ,Medicine ,Science - 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
- Published
- 2017
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