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

Authors

• Shennan Aibel Weiss, University of California Los Angeles
• Ali Akbar Asadi-Pooya, Thomas Jefferson UniversityFollow
• Sitaram Vangala, University of California Los Angeles
• Stephanie Moy, University of California Los Angeles
• Dale H. Wyeth, Thomas Jefferson UniversityFollow
• Iren Orosz, University of California Los Angeles
• Michael Gibbs, University of California Los Angeles
• Lara Schrader, University of California Los Angeles
• Jason Lerner, University of California Los Angeles
• Christopher K Cheng, University of California Los Angeles
• Edward Chang, University of California Los Angeles
• Rajsekar Rajaraman, University of California Los Angeles
• Inna Keselman, University of California Los Angeles
• Perdro Churchman, University of California Los Angeles
• Christine Bower-Baca, University of California Los Angeles
• Adam L Numis, University of California Los Angeles
• Michael G Ho, University of California Los Angeles
• Lekha Rao, University of California Los Angeles
• Annapoorna Bhat, Thomas Jefferson UniversityFollow
• Joanna Suski, Thomas Jefferson UniversityFollow
• Marjan Asadollahi, Thomas Jefferson UniversityFollow
• Timothy Ambrose, Thomas Jefferson UniversityFollow
• Andres Fernandez, Thomas Jefferson UniversityFollow
• Maromi Nei, Thomas Jefferson UniversityFollow
• Christopher T. Skidmore, Thomas Jefferson UniversityFollow
• Scott Mintzer, Thomas Jefferson UniversityFollow
• Dawn S. Eliashiv, Thomas Jefferson University
• Gary W Mathern, University of California Los Angeles
• Marc R Nuwer, University of California Los Angeles
• Michael R. Sperling, Thomas Jefferson UniversityFollow
• Jerome Engel, University of California Los AngelesFollow
• John M Stern, University of California Los Angeles

Document Type

Article

Publication Date

4-4-2017

Comments

This article has been peer reviewed. It is the author’s final published version in F1000Research

Volume 6, April 2017, Article number 30

The published version is available at DOI: 10.12688/f1000research.10569.2. Copyright © Weiss et al.

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

Recommended Citation

Weiss, Shennan Aibel; Asadi-Pooya, Ali Akbar; 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 T.; Mintzer, Scott; Eliashiv, Dawn S.; Mathern, Gary W; Nuwer, Marc R; Sperling, Michael R.; Engel, Jerome; and Stern, John M, "AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software." (2017). Department of Neurology Faculty Papers. Paper 130.
https://jdc.jefferson.edu/neurologyfp/130

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

28491280