The National Sleep Research Resource: towards a sleep data commons

Zhang, Guoqiang; Cui, Licong; Mueller, Remo; Tao, Shiqiang; Kim, Matthew; Rueschman, Michael; Mariani, Sara; Mobley, Daniel; Redline, Susan

doi:10.1093/jamia/ocy064

Cited by 525 publications

(365 citation statements)

References 28 publications

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“…Many authors concluded their paper suggesting that having access to more data would most likely improve the performance of their models. With large datasets becoming public, such as the TUH Dataset [65] and the National Sleep Research Resource [217], deeper architectures similar to the ones used in computer vision might become increasingly usable. However, it is important to note that the availability of data is quite different across domains.…”

Section: Datamentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Datamentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…We conducted a longitudinal retrospective study using data collected from the Sleep Heart Health Study (SHHS) [10][11][12][13].…”

Section: Methodsmentioning

confidence: 99%

Polysomnographic variables in Alternate overlap syndrome: data from sleep heart health study

Shrestha

Mukhtar

Kandel

et al. 2019

Journal of Community Hospital Internal Medicine Perspectives

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Objective: To evaluate influence of asthma on polysomnographic variables of patients with obstructive sleep apnea (OSA). Methods: A longitudinal retrospective study using data collected from the Sleep Heart Health Study (SHHS). Results: All 2822 patients included had OSA, 2599 were non-asthmatic whereas 223 were asthmatics. Average BMI for non-asthmatics was 28.8 kg/m 2 whereas asthmatics had 29.5 kg/ m 2 . Median pack-years of smoking was 1.42 vs. 1.98 in non-asthmatic and asthmatic groups, respectively. Sex distribution, age (in years), BMI, FEV 1 , FVC, AHI ≥ 4% (all apneas, hypopneas with ≥4% oxygen desaturation or arousal per hour of sleep), RDI ≥ 3% (overall respiratory distribution index at ≥3% oxygen desaturation or arousal), sleep latency, percentage of sleep time in apnea/hypopnea and Epworth sleep scale score were all statistically significant. Nonasthmatics had greater AHI (12.63/hr) compared to asthmatics (11.34/hour), p = 0.0015. RDI in non-asthmatics and asthmatics was (23.07 vs 20.53; p = 0.009). Sleep latency was found to be longer in asthmatics 19.8 minutes vs. 16 minutes (p = 0.008). Epworth sleepiness scale score was high in asthmatics (9 vs. 8, p = 0.002). Conclusion: OSA was found more severe in non-asthmatic subgroup, but asthmatics had statistically significant higher Epworth sleepiness scale score and sleep latency. Clinicians should be vigilant and keep low threshold to rule out OSA particularly on patients with difficult to control asthma, smoker, GERD, obese and nasal disease. ARTICLE HISTORY

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“…Participant data was accessed from the Cleveland Family Study (CFS) via the National Sleep Research Repository. [36][37][38][39] The CFS was conducted as a longitudinal study of sleep apnea and included 2284 individuals from 384 families over 16 years of follow up. All data were collected with written informed consent under protocols approved by a local institutional review board at each institution.…”

Section: Participantsmentioning

confidence: 99%

“…Given that slow wave activity is described to have different functions between stage N2 and stage N3 sleep, 45 we sought to characterize populations of slow waves from each sleep stage independently using surface EEG overnight recordings from subjects within the Cleveland Family Study. [36][37][38][39] Notably, the distinction between sleep stages is imperfect, and most often a mixing of lighter and deeper sleep occurs, particularly in transitions between sleep stages. 46,47 Further, individual variability of slow waves and spindles has been demonstrated with regard to frequency and timing aspects of coupling.…”

Section: Stage N2 and Stage N3 Slow Waves Demonstrate Distinct Composmentioning

confidence: 99%

“…Drawing from 582 subjects aged 6-88 years old from the Cleveland Family Study, [36][37][38][39] our analyses demonstrate that two subtypes of coupling, "early-fast" and "late-fast," can be identified during the upstates of slow waves. The early-fast coupling subtype preferentially occurs in stage N2 sleep during early life, with a gradual transition to a higher proportion of latefast coupling subtype as age advances.…”

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confidence: 99%

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The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

McConnell

Kronberg

Teale

et al. 2020

Preprint

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Study ObjectivesSlow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. MethodsCoupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. ResultsTwo different subtypes of spindles were identified during the upstates of (distinct) slow waves: an "early-fast" spindle, more common in stage N2 sleep, and a "late-fast" spindle, more common in stage N3. We further found stages N2 and N3 sleep are composed of two discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails during old age. ConclusionsDistinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep.Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease. Statement of SignificanceSlow waves of nonrapid eye movement sleep couple with sleep spindles in a process hypothesized to support memory functions. This coupling has recently gained interest as a possible biomarker of cognitive aging and onset of Alzheimer's disease. Most studies have been limited by an assumption that all slow waves (and coupled spindles) are fundamentally the same physiological events. Here we demonstrate that distinct subtypes of slow waves and their coupled spindles can be identified in human sleep. A mixture of different slow wave and spindle subtypes shifts in composition during lighter versus deeper sleep, and aging favors the deep sleep subtypes. These data should inform any future attempts to use slow wave sleep as a biomarker or clinical interventional target. Background: Slow waves occur during stages N2 and N3 Non-Rapid Eye Movement (NREM) sleep and are associated with large-scale synchronization of neuronal populations. 1-3 Spindlesare generated and propagated via cortical-thalamic loops, with timing input from the reticular nucleus of the thalamus. 4,5 In the process of aging, loss of slow waves and sleep spindles is highly correlated with cognitive decline, and abnormal slow wave neuronal circuitry is implicated in the pathogenesis of Alzheimer's disease 6 .

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The National Sleep Research Resource: towards a sleep data commons

Cited by 525 publications

References 28 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Polysomnographic variables in Alternate overlap syndrome: data from sleep heart health study

The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

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