A large collection of real-world pediatric sleep studies

Lee, Harlin; Li, Boyue; DeForte, Shelly; Splaingard, Mark; Huang, Yungui; Chi, Yuejie; Lin, Simon

doi:10.1038/s41597-022-01545-6

Cited by 20 publications

(18 citation statements)

References 25 publications

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“…We primarily used PSG data from two pediatric samples – the Nationwide Children’s Hospital Sleep DataBank (NCH) and the Child Adenotonsillectomy Trial (CHAT) – both available via the National Sleep Research Resource (http://sleepdata.org). The NCH sample was composed of patients (from infants to some adults) who underwent clinical PSG (Lee et al, 2022), and contained diagnostic (ICD 9/10 codes) and medication data. All the data were de-identified prior to NSRR deposition, and received NCH Institutional Review Board exemption with HIPAA waiver.…”

Section: Methodsmentioning

confidence: 99%

“…The DIAGNOSIS.csv file (available via NSRR) was used to delineate clinical sub-groups in the NCH sample. Following recommendations from the original description of the dataset (Lee et al, 2021), we only used final diagnosis codes (DX_ENC_TYPE & DX_SOURCE_TYPE columns equal to "Final Dx"). Since diagnostic codes provided for the sample were either according ICD9 or ICD10, we searched for specific diagnoses using the string search based on the diagnosis description (DX_NAME).…”

Section: Clinical Information For Nch Samplementioning

confidence: 99%

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Mapping Typical and Altered Neurodevelopment with Sleep Macro- and Micro-Architecture

Kozhemiako

Buckley

Chervin

et al. 2022

Preprint

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Profiles of sleep duration and timing and corresponding electroencephalographic activity reflect brain changes that support cognitive and behavioral maturation and may provide practical markers for tracking typical and atypical neurodevelopment. To build and evaluate a sleep-based, quantitative metric of brain maturation, we used whole-night polysomnography data, initially from two large National Sleep Research Resource samples, spanning childhood and adolescence (total N = 4,013, aged 2.5 to 17.5 years): the Childhood Adenotonsillectomy Trial (CHAT), a research study of children with snoring without neurodevelopmental delay, and NCH, a pediatric sleep clinic cohort. Among children without developmental disorders, sleep metrics derived from the electroencephalogram (EEG) displayed robust age-related changes consistently across datasets. Prominent stage-, band- and channel-specific developmental trajectories in spectral power were found. During non-rapid eye movement (NR) sleep, spindles and slow oscillations further exhibited characteristic developmental patterns, with respect to their rate of occurrence, temporal coupling and morphology. Based on these metrics in NCH, we constructed a model to predict an individual's chronological age. The model performed with high accuracy (r = 0.95 in the held-out NCH testing sample and r = 0.88 in a second independent replication sample (PATS) with a broadly comparable age range). EEG-based age predictions reflected clinically meaningful neurodevelopmental differences; for example, compared to typically developing children, those with neurodevelopmental diagnoses (NDD) showed greater variability in predicted age, and children with Down syndrome or intellectual disability had significantly younger brain age predictions (respectively, 2.2 and 0.59 years less than their chronological age) compared to age-matched non-NDD children. Overall, our results indicate that sleep architecture offers a sensitive window for characterizing brain maturation, suggesting the potential for scalable, objective sleep-based biomarkers to measure typical and atypical neurodevelopment.

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Section: Methodsmentioning

confidence: 99%

Section: Clinical Information For Nch Samplementioning

confidence: 99%

Mapping Typical and Altered Neurodevelopment with Sleep Macro- and Micro-Architecture

Kozhemiako

Buckley

Chervin

et al. 2022

Preprint

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“…The Nationwide Children’s Hospital (NCH) dataset was created to cover the lack of pediatric publicly available sleep studies [ 29 ]. It has sleep recordings from 3984 patients collected in a clinical environment, where most of them are children.…”

Section: Methodsmentioning

confidence: 99%

“…Seven EEG channels were employed in this study, namely C3-M2, O1-M2, O2-M1, CZ-O1, C4-M1, F4-M1, and F3-M2, as they are available in the recordings of ~99% of the participants [ 29 ]. For each EEG channel, recordings of participants from each age group were used to train two automatic sleep staging models, one for each automatic sleep staging algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

Nazih

Shahin

Eldesouki

et al. 2023

Sensors

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The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks).

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Development of generalizable automatic sleep staging using heart rate and movement based on large databases

Lee¹,

Kim

Lee

et al. 2023

Biomed. Eng. Lett.

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Purpose With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography. Methods A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen’s κ as key metrics. Results The fine-tuned model showed accuracy of 76.6% with Cohen’s κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen’s κ of 0.673 in another external validation dataset. Conclusion These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.

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A large collection of real-world pediatric sleep studies

Cited by 20 publications

References 25 publications

Mapping Typical and Altered Neurodevelopment with Sleep Macro- and Micro-Architecture

Mapping Typical and Altered Neurodevelopment with Sleep Macro- and Micro-Architecture

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

Development of generalizable automatic sleep staging using heart rate and movement based on large databases

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