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

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 SuskiMarjan Asadollahi, Timothy Ambrose, Andres Fernandez, Maromi Nei, Christopher Skidmore, Scott Mintzer, Dawn S. Eliashiv, Gary W. Mathern, Marc R. Nuwer, Michael Sperling, Jerome Engel, John M. Stern

Research output: Contribution to journalArticle

2 Citations (Scopus)

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<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.

Original languageEnglish (US)
Article number30
JournalF1000Research
Volume6
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Fingerprint

Electroencephalography
Artifacts
Muscle
Seizures
Software
Stroke
Muscles
Confidence Intervals
Scalp
Contamination

Keywords

  • Electroencephalogram
  • Independent component analysis
  • Muscle artifact
  • Scalp EEG
  • Seizure

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

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

In: F1000Research, Vol. 6, 30, 01.01.2017.

Research output: Contribution to journalArticle

Weiss, SA, Asadi-Pooya, AA, Vangala, S, Moy, S, Wyeth, DH, Orosz, I, Gibbs, M, Schrader, L, Lerner, J, Cheng, CK, Chang, E, Rajaraman, R, Keselman, I, Churchman, P, Bower-Baca, C, Numis, AL, Ho, MG, Rao, L, Bhat, A, Suski, J, Asadollahi, M, Ambrose, T, Fernandez, A, Nei, M, Skidmore, C, Mintzer, S, Eliashiv, DS, Mathern, GW, Nuwer, MR, Sperling, M, Engel, J & Stern, JM 2017, 'AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software', F1000Research, vol. 6, 30. https://doi.org/10.12688/f1000research.10569.2
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, Jerome ; 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. In: F1000Research. 2017 ; Vol. 6.
@article{65d77e39774f4903bcc80fa00a579523,
title = "AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software",
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<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.",
keywords = "Electroencephalogram, Independent component analysis, Muscle artifact, Scalp EEG, Seizure",
author = "Weiss, {Shennan Aibel} and Asadi-Pooya, {Ali A.} and Sitaram Vangala and Stephanie Moy and Wyeth, {Dale H.} and Iren Orosz and Michael Gibbs and Lara Schrader and Jason Lerner and Cheng, {Christopher K.} and Edward Chang and Rajsekar Rajaraman and Inna Keselman and Perdro Churchman and Christine Bower-Baca and Numis, {Adam L.} and Ho, {Michael G.} and Lekha Rao and Annapoorna Bhat and Joanna Suski and Marjan Asadollahi and Timothy Ambrose and Andres Fernandez and Maromi Nei and Christopher Skidmore and Scott Mintzer and Eliashiv, {Dawn S.} and Mathern, {Gary W.} and Nuwer, {Marc R.} and Michael Sperling and Jerome Engel and Stern, {John M.}",
year = "2017",
month = "1",
day = "1",
doi = "10.12688/f1000research.10569.2",
language = "English (US)",
volume = "6",
journal = "F1000Research",
issn = "2046-1402",
publisher = "F1000 Research Ltd.",

}

TY - JOUR

T1 - AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation

T2 - Validation and comparison of performance with commercially available software

AU - Weiss, Shennan Aibel

AU - Asadi-Pooya, Ali A.

AU - Vangala, Sitaram

AU - Moy, Stephanie

AU - Wyeth, Dale H.

AU - Orosz, Iren

AU - Gibbs, Michael

AU - Schrader, Lara

AU - Lerner, Jason

AU - Cheng, Christopher K.

AU - Chang, Edward

AU - Rajaraman, Rajsekar

AU - Keselman, Inna

AU - Churchman, Perdro

AU - Bower-Baca, Christine

AU - Numis, Adam L.

AU - Ho, Michael G.

AU - Rao, Lekha

AU - Bhat, Annapoorna

AU - Suski, Joanna

AU - Asadollahi, Marjan

AU - Ambrose, Timothy

AU - Fernandez, Andres

AU - Nei, Maromi

AU - Skidmore, Christopher

AU - Mintzer, Scott

AU - Eliashiv, Dawn S.

AU - Mathern, Gary W.

AU - Nuwer, Marc R.

AU - Sperling, Michael

AU - Engel, Jerome

AU - Stern, John M.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - Electroencephalogram

KW - Independent component analysis

KW - Muscle artifact

KW - Scalp EEG

KW - Seizure

UR - http://www.scopus.com/inward/record.url?scp=85018268963&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85018268963&partnerID=8YFLogxK

U2 - 10.12688/f1000research.10569.2

DO - 10.12688/f1000research.10569.2

M3 - Article

AN - SCOPUS:85018268963

VL - 6

JO - F1000Research

JF - F1000Research

SN - 2046-1402

M1 - 30

ER -