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]
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. Metho...
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F1000 Research Ltd
2017-04-01
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Online Access: | https://f1000research.com/articles/6-30/v2 |
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doaj-6e900f7054d147ec93446194a75c468f |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 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] F1000Research Neuroimaging |
author_facet |
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 |
author_sort |
Shennan Aibel Weiss |
title |
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] |
title_short |
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] |
title_full |
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] |
title_fullStr |
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] |
title_full_unstemmed |
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] |
title_sort |
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] |
publisher |
F1000 Research Ltd |
series |
F1000Research |
issn |
2046-1402 |
publishDate |
2017-04-01 |
description |
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. |
topic |
Neuroimaging |
url |
https://f1000research.com/articles/6-30/v2 |
work_keys_str_mv |
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doaj-6e900f7054d147ec93446194a75c468f2020-11-25T02:48:21ZengF1000 Research LtdF1000Research2046-14022017-04-01610.12688/f1000research.10569.212199AR2, 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 Weiss0Ali A Asadi-Pooya1Sitaram Vangala2Stephanie Moy3Dale H Wyeth4Iren Orosz5Michael Gibbs6Lara Schrader7Jason Lerner8Christopher K Cheng9Edward Chang10Rajsekar Rajaraman11Inna Keselman12Perdro Churchman13Christine Bower-Baca14Adam L Numis15Michael G Ho16Lekha Rao17Annapoorna Bhat18Joanna Suski19Marjan Asadollahi20Timothy Ambrose21Andres Fernandez22Maromi Nei23Christopher Skidmore24Scott Mintzer25Dawn S Eliashiv26Gary W Mathern27Marc R Nuwer28Michael Sperling29Jerome Engel Jr30John M Stern31Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Medicine, Statistics Core, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Radiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartments of Neurosurgery, Psychiatry, and Biobehavioral Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USADepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USAObjective: 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.https://f1000research.com/articles/6-30/v2Neuroimaging |