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Sleep Apnea Syndromes: HELP
Articles by Anna E. Mullins
Based on 2 articles published since 2010
(Why 2 articles?)

Between 2010 and 2020, Anna E. Mullins wrote the following 2 articles about Sleep Apnea Syndromes.
+ Citations + Abstracts
1 Article Intra-individual stability of NREM sleep quantitative EEG measures in obstructive sleep apnea. 2019

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. ·Sydney Medical School, University of Sydney, Sydney, Australia. · CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, Australia. · NeuroSleep, NHMRC Centre of Research Excellence, Sydney, Australia. · Sydney Local Health District, Sydney, Australia. · Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Sydney, Australia. · University of Sydney Nursing School, Sydney, Australia. · Department of Healthcare IT, Inje University, Inje-ro 197, Kimhae, Kyunsangnam-do, South Korea. · School of Psychology, University of Sydney, Sydney, Australia. ·J Sleep Res · Pubmed #30821056.

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.

2 Article Slow-wave activity surrounding stage N2 K-complexes and daytime function measured by psychomotor vigilance test in obstructive sleep apnea. 2019

Parekh, Ankit / Mullins, Anna E / Kam, Korey / Varga, Andrew W / Rapoport, David M / Ayappa, Indu. ·Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. ·Sleep · Pubmed #30561750.

ABSTRACT: STUDY OBJECTIVE: To better understand the inter-individual differences in neurobehavioral impairment in obstructive sleep apnea (OSA) and its treatment with continuous positive airway pressure (CPAP), we examined how changes in sleep electroencephalography (EEG) slow waves were associated with next-day psychomotor vigilance test (PVT) performance. METHODS: Data from 28 OSA subjects (Apnea-Hypopnea Index with 3% desaturation and/or with an associated arousal [AHI3A] > 15/hour; AHI3A = sum of all apneas and hypopneas with 3% O2 desaturation and/or an EEG arousal, divided by total sleep time [TST]), who underwent three full in-lab nocturnal polysomnographies (NPSGs: chronic OSA, CPAP-treated OSA, and acute OSA), and 19 healthy sleepers were assessed. Four 20-minute PVTs were performed after each NPSG along with subjective and objective assessment of sleepiness. Three EEG metrics were calculated: K-complex (KC) Density (#/minute of N2 sleep), change in slow-wave activity in 1-second envelopes surrounding KCs (ΔSWAK), and relative frontal slow-wave activity during non-rapid eye movement (NREM) (%SWA). RESULTS: CPAP treatment of OSA resulted in a decrease in KC Density (chronic: 3.9 ± 2.2 vs. treated: 2.7 ± 1.1; p < 0.01; mean ± SD) and an increase in ΔSWAK (chronic: 2.6 ± 2.3 vs. treated: 4.1 ± 2.4; p < 0.01) and %SWA (chronic: 20.9 ± 8.8 vs. treated: 26.6 ± 8.6; p < 0.001). Cross-sectionally, lower ΔSWAK values were associated with higher PVT Lapses (chronic: rho = -0.55, p < 0.01; acute: rho = -0.46, p = 0.03). Longitudinally, improvement in PVT Lapses with CPAP was associated with an increase in ΔSWAK (chronic to treated: rho = -0.48, p = 0.02; acute to treated: rho = -0.5, p = 0.03). In contrast, OSA severity or global sleep quality metrics such as arousal index, NREM, REM, or TST were inconsistently associated with PVT Lapses. CONCLUSION: Changes in EEG slow waves, in particular ∆SWAK, explain inter-individual differences in PVT performance better than conventional NPSG metrics, suggesting that ΔSWAK is a night-time correlate of next-day vigilance in OSA.