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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

  • Zhang Z Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Switzerland.
  • Mayer G Neurology Department, Hephata Klinik, Schwalmstadt, Germany.
  • Dauvilliers Y Centre de Reference Nationale Maladies Rares, Narcolepsie et Hypersomnie Idiopathique, Service Neurologie, Hôpital Gui-de-Chauliac, INSERM U1061, Montpellier, France.
  • Plazzi G Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Pizza F Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Fronczek R Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Santamaria J Neurology Service, Multidisciplinary Sleep Unit, Hospital Clínic of Barcelona, IDIBAPS, CIBERNED, Barcelona, Spain.
  • Partinen M Helsinki Sleep Clinic, Vitalmed Research Center, Helsinki, Finland.
  • Overeem S Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands.
  • Peraita-Adrados R Sleep and Epilepsy Unit, Clinical Neurophysiology Department, Gregorio Marañón University Hospital, Complutense University of Madrid (UCM), Madrid, Spain.
  • da Silva AM Serviço de Neurofisiologia, Hospital Santo António/Centro Hospitalar do Porto and Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.
  • Sonka K Neurology Department and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
  • Rio-Villegas RD Unidad de Neurofisiología y Trastornos del Sueño. Hospital Vithas Internacional Madrid, Madrid, Spain.
  • Heinzer R Center for Investigation and Research in Sleep, Lausanne University Hospital, Lausanne, Switzerland.
  • Wierzbicka A Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland.
  • Young P Department of Sleep Medicine and Neuromuscular Disorders, University of Münster, Münster, Germany.
  • Högl B Neurology Department, Sleep Disorders Clinic, Medical University of Innsbruck, Innsbruck, Austria.
  • Bassetti CL Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland.
  • Manconi M Sleep and Epilepsy Center, Neurocenter of Southern Switzerland, Lugano, Switzerland.
  • Feketeova E Neurology Department, Medical Faculty of P. J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic.
  • Mathis J Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland.
  • Paiva T Institute of Molecular Medicine Portugal, Medical Faculty Lisbon University, Lisbon, Portugal.
  • Canellas F Fundació Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma de Mallorca, Spain.
  • Lecendreux M AP-HP, Pediatric Sleep Center, CHU Robert-Debré, Paris, France.
  • Baumann CR Neurology Department, University Hospital Zurich, Zurich, Switzerland.
  • Barateau L Centre de Reference Nationale Maladies Rares, Narcolepsie et Hypersomnie Idiopathique, Service Neurologie, Hôpital Gui-de-Chauliac, INSERM U1061, Montpellier, France.
  • Pesenti C Centre de Reference Nationale Maladies Rares, Narcolepsie et Hypersomnie Idiopathique, Service Neurologie, Hôpital Gui-de-Chauliac, INSERM U1061, Montpellier, France.
  • Antelmi E Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum, University of Bologna, Bologna, Italy.
  • Gaig C Neurology Service, Multidisciplinary Sleep Unit, Hospital Clínic of Barcelona, IDIBAPS, CIBERNED, Barcelona, Spain.
  • Iranzo A Neurology Service, Multidisciplinary Sleep Unit, Hospital Clínic of Barcelona, IDIBAPS, CIBERNED, Barcelona, Spain.
  • Lillo-Triguero L Sleep and Epilepsy Unit, Clinical Neurophysiology Department, Gregorio Marañón University Hospital, Complutense University of Madrid (UCM), Madrid, Spain.
  • Medrano-Martínez P Sleep and Epilepsy Unit, Clinical Neurophysiology Department, Gregorio Marañón University Hospital, Complutense University of Madrid (UCM), Madrid, Spain.
  • Haba-Rubio J Center for Investigation and Research in Sleep, Lausanne University Hospital, Lausanne, Switzerland.
  • Gorban C Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Switzerland.
  • Luca G Centre Neuchatelois de Psychiatrie, Neuchatel, Switzerland.
  • Lammers GJ Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Khatami R Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Switzerland. ramin.khatami@barmelweid.ch.
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  • 2018-07-15
Published in:
  • Scientific reports. - 2018
Adult
Data Interpretation, Statistical
Databases, Factual
Datasets as Topic
Female
Humans
Male
Models, Biological
Narcolepsy
Polysomnography
ROC Curve
Rare Diseases
Sleep Latency
Sleep, REM
Stochastic Processes
Supervised Machine Learning
Young Adult
English 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 poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/278752
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