AIMC Topic: Narcolepsy

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Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

Sleep medicine May 23, 2024
BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods f...
Comorbidity Narcolepsy Machine Learning Female Adult Male Middle Aged Depressive Disorder, Major Humans
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Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

Journal of sleep research Apr 26, 2024
The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understandi...
Humans Machine Learning Male Middle Aged Diagnosis, Differential Female Young Adult Adult Polysomnography Idiopathic Hypersomnia Sleep Narcolepsy
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Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Nature communications Dec 6, 2018
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 abn...
HLA-DQ beta-Chains Polysomnography Sleep Stages Sensitivity and Specificity Male Middle Aged Adolescent Cohort Studies Narcolepsy Aged Young Adult Adult Algorithms Female Humans Neural Networks, Computer
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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports Jul 13, 2018
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...
Female Young Adult Adult Datasets as Topic Narcolepsy Sleep Latency Male Humans Stochastic Processes ROC Curve Polysomnography Rare Diseases Models, Biological Sleep, REM Data Interpretation, Statistical Databases, Factual Supervised Machine Learning
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Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy.

Journal of neuroscience methods Feb 20, 2017
BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized ...
Sleep Stages Polysomnography Electrooculography Adult Sensitivity and Specificity Electromyography Humans Middle Aged Linear Models Cohort Studies Machine Learning Narcolepsy Discriminant Analysis Electroencephalography
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Clinical course of H1N1-vaccine-related narcolepsy.

Sleep medicine Nov 25, 2015
OBJECTIVE: To follow and analyze the clinical course and quality of life of Pandemrix H1N1-vaccine-related narcolepsy (pNT1).
Influenza, Human Narcolepsy Humans Influenza Vaccines Male Influenza A Virus, H1N1 Subtype Female Orexins Surveys and Questionnaires Polysomnography Adolescent
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[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].

Zhonghua yi xue za zhi Jul 16, 2024
To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. A cross-sectional study. Twenty-five narcolepsy type 1 patients who wer...
Neural Networks, Computer Female Cross-Sectional Studies Face Humans Male Narcolepsy Deep Learning Cataplexy Image Processing, Computer-Assisted
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