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Illustration de l'actualité : PhD defense Stanislas Chambon : Learning from electrophysiology time series during sleep: from scoring to event detection

PhD defense Stanislas Chambon : Learning from electrophysiology time series during sleep: from scoring to event detection

vendredi
14
décembre
2018

PhD Comics : I'm defending my thesis, Mom !

14:00, amphi Emeraude

Jury

  • M. Alexandre GRAMFORT, Inria Saclay, Supervisor
  • M. Alain RAKOTOMAMONJY, Université de Rouen, Reviewer
  • M. Maarten DE VOS, Oxford University, Reviewer
  • M. Aldo FAISAL, Imperial College London, Examiner
  • M. Jérémie MATTOUT, INSERM - CRNL, Examiner
  • M. Marco CUTURI, ENSAE - Université Paris Saclay, Examiner
  • Mme. Michèle SEBAG, CNRS - LRI, Examiner
  • M. Pierrick ARNAL, Dreem, Guest

Abstract

Sleep is a complex and not fully understood biological phenomenon. The traditional process to monitor sleep relies on the polysomnography exam (PSG). It records, in a non invasive fashion at the level of the skin, electrophysiological modifications of the brain activity (electroencephalography, EEG), ocular (electro-oculography, EOG) and muscular (electro-myography, EMG). The recorded signals are then analyzed by a sleep expert who manually annotates the events of interest such as the sleep stages or some micro-events. However, manual labeling is time-consuming and prone to the expert subjectivity. Furthermore, the development of sleep monitoring consumer wearable devices which record and process automatically electrophysiological signals, such as Dreem headband, requires to automate some labeling tasks.

for sleep sciences. On the other hand, this is also raising new concerns related to the scarcity of labeled data that may prevent their training processes and the variability of data that may hurt their performances. Indeed, sleep data is scarce due to the labeling burden and exhibits also some intra and inter-subject variability (due to sleep disorders, aging...).