Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

By Jens B. Stephansen, A. N. Olesen, Mads Olsen, A. Ambati, E. Leary, Hyatt Moore, O. Carrillo, Ling Lin, F. Han, Han Yan and 20 more. Nature Communications, 2017

307 citations recorded by the source corpus.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies. The diagnosis of sleep disorders such as narcolepsy and insomnia currently requires experts to interpret sleep recordings (polysomnography). Here, the authors introduce a neural network analysis method for polysomnography that could reduce time spent in sleep clinics and automate narcolepsy diagnosis.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ef490b0a42b6d3f413e03dd13d5251aab5acdb82

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Citations in the corpus

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, methodology): “There are two main categories: the first directly processes 1-dimensional raw signals [17], [20], [22], [24], [26], [27], [28] and the second ingests 2-dimensional time-frequency images as inputs [10], [11], [18], [19].”
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, background): “These advances have significantly pushed the performance of machine sleep scoring to be on par with human scoring [12], [15], [16], [26].”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, background): “…work shows that such systems can be highly robust (even compared to human performance) and may play an important role in developing novel biomarkers for sleep disorders and other (e.g., neurodegenerative and psychiatric) diseases [Stephansen et al., 2018, Warby et al., 2014, Schenck et al., 2014].”
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (2018, unknown): “With the deep learning methods evolving, automatic sleep staging performance has been boosted considerably as state-of-the-art results have been reported on several datasets [8], [9], [12], [13].”
An open-source, high-performance tool for automated sleep staging (2021, methodology): “The Stephansen et al., 2018 and Perslev et al., 2021 columns show the performance of two recent deep- learning-based sleep-staging algorithms ( Perslev et al., 2021 ; Stephansen et al., 2018 ).”