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An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing

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Abstract

Purpose

Large quantities of neurophysiological electroencephalogram (EEG) data are routinely collected in the sleep laboratory. These are underutilised due to the burden of managing artefact contamination. The aim of this study was to develop a new tool for automated artefact rejection that facilitates subsequent quantitative analysis of sleep EEG data collected during routine overnight polysomnography (PSG) in subjects with and without sleep-disordered breathing (SDB).

Methods

We evaluated the accuracy of an automated algorithm to detect sleep EEG artefacts against artefacts manually scored by three experienced technologists (reference standard) in 40 PSGs. Spectral power was computed using artefact-free EEG data derived from (1) the reference standard, (2) the algorithm and (3) raw EEG without any prior artefact rejection.

Results

The algorithm showed a high level of accuracy of 94.3, 94.7 and 95.8 % for detecting artefacts during the entire PSG, NREM sleep and REM sleep, respectively. There was good to moderate sensitivity and excellent specificity of the algorithm detection capabilities during sleep. The EEG spectral power for the reference standard and algorithm was significantly lower than that of the raw, unprocessed EEG signal.

Conclusions

These preliminary findings support an automated way to process EEG artefacts during sleep, providing the opportunity to investigate EEG-based markers of neurobehavioural impairment in sleep disorders in future studies.

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Acknowledgments

A.L.D. was supported by an Australian National Health and Medical Research Council (NHMRC) Dora Lush Priority Scholarship (633172) and CIRUS Scholarship. P.Y.L. was supported by a NHMRC Senior Research Fellowship (1025248). K.K.H.W. was supported by a RACP-CONROD Fellowship. R.K. was supported by a NHMRC Postgraduate Medical Scholarship (633161) and CIRUS Scholarship. R.R.G. was supported by a NHMRC Practitioner Fellowship (1022730). J.W.K. was supported by a CIRUS Fellowship.

Conflict of interest

The authors declare that they have no conflicts of interest.

Author information

Authors and Affiliations

  1. Sleep and Circadian Research Group, Woolcock Institute of Medical Research and NHMRC Centre for Integrated Research and Understanding of Sleep (CIRUS), The University of Sydney, Sydney, NSW, Australia

    Angela L. D’Rozario, George C. Dungan II, Peter Y. Liu, Keith K. H. Wong, Roo Killick, Ronald R. Grunstein & Jong Won Kim

  2. Sydney Local Health District, Camperdown, Sydney, NSW, Australia

    Angela L. D’Rozario

  3. Centre for Sleep Research, School of Psychology, Social Work and Social Policy, University of South Australia, Adelaide, SA, Australia

    Siobhan Banks

  4. Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA, USA

    Peter Y. Liu

  5. Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Sydney, Australia

    Keith K. H. Wong & Ronald R. Grunstein

  6. School of Physics, The University of Sydney, Sydney, Australia

    Jong Won Kim

  7. Brain Dynamics Centre, Sydney Medical School-Western, The University of Sydney, Sydney, Australia

    Jong Won Kim

  8. Woolcock Institute of Medical Research, PO Box M77, Missenden Road, Sydney, NSW, 2050, Australia

    Angela L. D’Rozario

Authors
  1. Angela L. D’Rozario
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  2. George C. Dungan II
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  3. Siobhan Banks
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  6. Roo Killick
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  7. Ronald R. Grunstein
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  8. Jong Won Kim
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Correspondence to Angela L. D’Rozario.

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D’Rozario, A.L., Dungan, G.C., Banks, S. et al. An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing. Sleep Breath 19, 607–615 (2015). https://doi.org/10.1007/s11325-014-1056-z

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  • DOI: https://doi.org/10.1007/s11325-014-1056-z

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