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]
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,
John M Stern
Affiliations
Shennan Aibel Weiss
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Ali A Asadi-Pooya
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Sitaram Vangala
Department of Medicine, Statistics Core, University of California Los Angeles, Los Angeles, USA
Stephanie Moy
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Dale H Wyeth
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Iren Orosz
Department of Radiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Michael Gibbs
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Lara Schrader
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Jason Lerner
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Christopher K Cheng
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Edward Chang
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Rajsekar Rajaraman
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Inna Keselman
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Perdro Churchman
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Christine Bower-Baca
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Adam L Numis
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Michael G Ho
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Lekha Rao
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Annapoorna Bhat
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Joanna Suski
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Marjan Asadollahi
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Timothy Ambrose
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Andres Fernandez
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Maromi Nei
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Christopher Skidmore
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Scott Mintzer
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Dawn S Eliashiv
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Gary W Mathern
Departments of Neurosurgery, Psychiatry, and Biobehavioral Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Marc R Nuwer
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
Michael Sperling
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USA
Jerome Engel Jr
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
John M Stern
Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
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.
