Intra‐individual stability of NREM sleep quantitative EEG measures in obstructive sleep apnea

Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea–hypopnea indices. Quantitative analysis of these data (quantitative electroencephalogram) may provide trait‐like information to predict patient vulne...

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Published inJournal of sleep research Vol. 28; no. 6; pp. e12838 - n/a
Main Authors Poon, Joseph J. Y., Chapman, Julia L., Wong, Keith K. H., Mullins, Anna E., Cho, Garry, Kim, Jong W., Yee, Brendon J., Grunstein, Ronald R., Marshall, Nathaniel S., D'Rozario, Angela L.
Format Journal Article
LanguageEnglish
Published England 01.12.2019
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Abstract Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea–hypopnea indices. Quantitative analysis of these data (quantitative electroencephalogram) may provide trait‐like information to predict patient vulnerability to sleepiness. Measurements of trait‐like characteristics need to have high test–retest reliability. We aimed to investigate the intra‐individual stability of slow‐wave (delta power) and spindle frequency (sigma power) activity during non‐rapid eye movement sleep in patients with obstructive sleep apnea. We recorded sleep electroencephalograms during two overnight polysomnographic recordings in 61 patients with obstructive sleep apnea (median days between studies 47, inter‐quartile range 53). Electroencephalograms recorded at C3‐M2 derivation were quantitatively analysed using power spectral analysis following artefact removal. Relative delta (0.5–4.5 Hz) and sigma (12–15 Hz) power during non‐rapid eye movement sleep were calculated. Intra‐class correlation coefficients and Bland–Altman plots were used to assess agreement between nights. Intra‐class correlation coefficients demonstrated good‐to‐excellent agreement in the delta and sigma frequencies between nights (intra‐class correlation coefficients: 0.84, 0.89, respectively). Bland–Altman analysis of delta power showed a mean difference close to zero (−0.4, 95% limits of agreement −9.4, 8.7) and no heteroscedasticity with increasing power. Sigma power demonstrated heteroscedasticity, with reduced stability as sigma power increased. The mean difference of sigma power between nights was close to zero (0.1, 95% limits −1.6, 1.8). We have demonstrated the stability of slow‐wave and spindle frequency electroencephalograms during non‐rapid eye movement sleep within patients with obstructive sleep apnea. The electroencephalogram profile during non‐rapid eye movement sleep may be a useful biomarker for predicting vulnerability to daytime impairment in obstructive sleep apnea and responsiveness to treatment.
AbstractList Abstract Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea–hypopnea indices. Quantitative analysis of these data ( quantitative electroencephalogram ) may provide trait‐like information to predict patient vulnerability to sleepiness. Measurements of trait‐like characteristics need to have high test–retest reliability. We aimed to investigate the intra‐individual stability of slow‐wave (delta power) and spindle frequency (sigma power) activity during non‐rapid eye movement sleep in patients with obstructive sleep apnea. We recorded sleep electroencephalograms during two overnight polysomnographic recordings in 61 patients with obstructive sleep apnea (median days between studies 47, inter‐quartile range 53). Electroencephalograms recorded at C3‐M2 derivation were quantitatively analysed using power spectral analysis following artefact removal. Relative delta (0.5–4.5 Hz) and sigma (12–15 Hz) power during non‐rapid eye movement sleep were calculated. Intra‐class correlation coefficients and Bland–Altman plots were used to assess agreement between nights. Intra‐class correlation coefficients demonstrated good‐to‐excellent agreement in the delta and sigma frequencies between nights (intra‐class correlation coefficients: 0.84, 0.89, respectively). Bland–Altman analysis of delta power showed a mean difference close to zero (−0.4, 95% limits of agreement −9.4, 8.7) and no heteroscedasticity with increasing power. Sigma power demonstrated heteroscedasticity, with reduced stability as sigma power increased. The mean difference of sigma power between nights was close to zero (0.1, 95% limits −1.6, 1.8). We have demonstrated the stability of slow‐wave and spindle frequency electroencephalograms during non‐rapid eye movement sleep within patients with obstructive sleep apnea. The electroencephalogram profile during non‐rapid eye movement sleep may be a useful biomarker for predicting vulnerability to daytime impairment in obstructive sleep apnea and responsiveness to treatment.
Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea-hypopnea indices. Quantitative analysis of these data (quantitative electroencephalogram) may provide trait-like information to predict patient vulnerability to sleepiness. Measurements of trait-like characteristics need to have high test-retest reliability. We aimed to investigate the intra-individual stability of slow-wave (delta power) and spindle frequency (sigma power) activity during non-rapid eye movement sleep in patients with obstructive sleep apnea. We recorded sleep electroencephalograms during two overnight polysomnographic recordings in 61 patients with obstructive sleep apnea (median days between studies 47, inter-quartile range 53). Electroencephalograms recorded at C3-M2 derivation were quantitatively analysed using power spectral analysis following artefact removal. Relative delta (0.5-4.5 Hz) and sigma (12-15 Hz) power during non-rapid eye movement sleep were calculated. Intra-class correlation coefficients and Bland-Altman plots were used to assess agreement between nights. Intra-class correlation coefficients demonstrated good-to-excellent agreement in the delta and sigma frequencies between nights (intra-class correlation coefficients: 0.84, 0.89, respectively). Bland-Altman analysis of delta power showed a mean difference close to zero (-0.4, 95% limits of agreement -9.4, 8.7) and no heteroscedasticity with increasing power. Sigma power demonstrated heteroscedasticity, with reduced stability as sigma power increased. The mean difference of sigma power between nights was close to zero (0.1, 95% limits -1.6, 1.8). We have demonstrated the stability of slow-wave and spindle frequency electroencephalograms during non-rapid eye movement sleep within patients with obstructive sleep apnea. The electroencephalogram profile during non-rapid eye movement sleep may be a useful biomarker for predicting vulnerability to daytime impairment in obstructive sleep apnea and responsiveness to treatment.
Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea–hypopnea indices. Quantitative analysis of these data (quantitative electroencephalogram) may provide trait‐like information to predict patient vulnerability to sleepiness. Measurements of trait‐like characteristics need to have high test–retest reliability. We aimed to investigate the intra‐individual stability of slow‐wave (delta power) and spindle frequency (sigma power) activity during non‐rapid eye movement sleep in patients with obstructive sleep apnea. We recorded sleep electroencephalograms during two overnight polysomnographic recordings in 61 patients with obstructive sleep apnea (median days between studies 47, inter‐quartile range 53). Electroencephalograms recorded at C3‐M2 derivation were quantitatively analysed using power spectral analysis following artefact removal. Relative delta (0.5–4.5 Hz) and sigma (12–15 Hz) power during non‐rapid eye movement sleep were calculated. Intra‐class correlation coefficients and Bland–Altman plots were used to assess agreement between nights. Intra‐class correlation coefficients demonstrated good‐to‐excellent agreement in the delta and sigma frequencies between nights (intra‐class correlation coefficients: 0.84, 0.89, respectively). Bland–Altman analysis of delta power showed a mean difference close to zero (−0.4, 95% limits of agreement −9.4, 8.7) and no heteroscedasticity with increasing power. Sigma power demonstrated heteroscedasticity, with reduced stability as sigma power increased. The mean difference of sigma power between nights was close to zero (0.1, 95% limits −1.6, 1.8). We have demonstrated the stability of slow‐wave and spindle frequency electroencephalograms during non‐rapid eye movement sleep within patients with obstructive sleep apnea. The electroencephalogram profile during non‐rapid eye movement sleep may be a useful biomarker for predicting vulnerability to daytime impairment in obstructive sleep apnea and responsiveness to treatment.
Author Mullins, Anna E.
Wong, Keith K. H.
Poon, Joseph J. Y.
D'Rozario, Angela L.
Cho, Garry
Grunstein, Ronald R.
Kim, Jong W.
Chapman, Julia L.
Yee, Brendon J.
Marshall, Nathaniel S.
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Keywords obstructive sleep apnea
electroencephalography
agreement
Bland-Altman analysis
test-retest reliability
power spectral analysis
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Snippet Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea–hypopnea...
Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea-hypopnea...
Abstract Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate...
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StartPage e12838
SubjectTerms Adult
Aged
agreement
Bland–Altman analysis
electroencephalography
Electroencephalography - methods
Electroencephalography - standards
Female
Humans
Individuality
Male
Middle Aged
obstructive sleep apnea
Polysomnography - methods
Polysomnography - standards
power spectral analysis
Reproducibility of Results
Sleep Apnea, Obstructive - diagnosis
Sleep Apnea, Obstructive - physiopathology
Sleep Stages - physiology
test–retest reliability
Wakefulness - physiology
Title Intra‐individual stability of NREM sleep quantitative EEG measures in obstructive sleep apnea
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fjsr.12838
https://www.ncbi.nlm.nih.gov/pubmed/30821056
https://search.proquest.com/docview/2187522639
Volume 28
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