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

16 May 2019

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

8 / 8 papers shown

Title
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal Stanislas Chambon Valentin Thorey P. Arnal Emmanuel Mignot Alexandre Gramfort 97 59 0 07 Dec 2018
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms A. N. Olesen P. Jennum P. Peppard Emmanuel Mignot H. Sørensen 50 26 0 08 Oct 2018
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging Huy P Phan Fernando Andreotti Navin Cooray Oliver Y. Chén M. D. Vos 101 418 0 28 Sep 2018
A deep learning architecture to detect events in EEG signals during sleep Stanislas Chambon Valentin Thorey P. Arnal Emmanuel Mignot Alexandre Gramfort 121 41 0 11 Jul 2018
Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy Jens B. Stephansen A. N. Olesen Mads Olsen A. Ambati E. Leary ... C. Kushida P. Peppard H. Sørensen P. Jennum Emmanuel Mignot 63 226 0 05 Oct 2017
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series Stanislas Chambon M. Galtier P. Arnal G. Wainrib Alexandre Gramfort MLAU AI4TS 63 489 0 05 Jul 2017
SSD: Single Shot MultiBox Detector Wen Liu Dragomir Anguelov D. Erhan Christian Szegedy Scott E. Reed Cheng-Yang Fu Alexander C. Berg ObjD BDL 229 29,830 0 08 Dec 2015
You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon S. Divvala Ross B. Girshick Ali Farhadi ObjD 697 36,958 0 08 Jun 2015