Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy

Abstract

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 instead of 30 second 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.

arXiv comment21 pages (not including title or references), 6 figures (1a - 6c), 6 tables, 5 supplementary figures, 9 supplementary tables

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• Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
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• Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
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• Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring
• Addressing database variability in learning from medical data: an ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring
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• SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
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• Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
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• SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features
• aSAGA: Automatic Sleep Analysis with Gray Areas
• Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
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• sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging
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