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Towards a Flexible Deep Learning Method for Automatic Detection of
Clinically Relevant Multi-Modal Events in the Polysomnogram

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019

16 May 2019

Stanislas Chambon

Valentin Thorey

Emmanuel Mignot

ArXiv (abs)PDF HTML

Papers citing "Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram"

4 / 4 papers shown

MSED: a multi-modal sleep event detection model for clinical sleep
analysis

MSED: a multi-modal sleep event detection model for clinical sleep analysisIEEE Transactions on Biomedical Engineering (IEEE Trans. Biomed. Eng.), 2021

Alexander Neergaard Zahid

Emmanuel Mignot

265

30

0

07 Jan 2021

Automatic sleep stage classification with deep residual networks in a
mixed-cohort setting

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Emmanuel Mignot

218

88

0

21 Aug 2020

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

XSleepNet: Multi-View Sequential Model for Automatic Sleep StagingIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020

Oliver Y. Chén

492

292

0

08 Jul 2020

Deep transfer learning for improving single-EEG arousal detection

Deep transfer learning for improving single-EEG arousal detectionAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020

Emmanuel Mignot

339

13

0

10 Apr 2020

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