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Sleep Med. 2018 Jul;47:126-136. doi: 10.1016/j.sleep.2017.11.1128. Epub 2017 Nov 29.

Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource.

Author information

1
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: smariani@partners.org.
2
University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Chronobiology and Sleep Research, Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
3
Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Beth Israel Deaconess Medical Center, Boston, MA, USA.
4
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
5
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
6
University of California, San Francisco School of Medicine, San Francisco, CA, USA.
7
Research Institute, California Pacific Medical Center, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
8
Case Western Reserve University, Cleveland, OH, USA.
9
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Case Western Reserve University, Cleveland, OH, USA.
10
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Division of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany.

Abstract

STUDY OBJECTIVES:

We present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach).

METHODS:

EEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots.

RESULTS:

Assessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach.

CONCLUSION:

Despite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.

KEYWORDS:

Artifact detection; Large-scale spectral analysis; Sleep EEG

PMID:
29803181
PMCID:
PMC5976521
[Available on 2019-07-01]
DOI:
10.1016/j.sleep.2017.11.1128

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