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Neural network an1alysis of sleep stages enables efficient diagnosis of
  narcolepsy

Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy

5 October 2017
Jens B. Stephansen
A. N. Olesen
Mads Olsen
A. Ambati
E. Leary
Hyatt Moore
O. Carrillo
Ling Lin
F. Han
Han Yan
Yunliang Sun
Y. Dauvilliers
Sabine Scholz
L. Barateau
B. Hogl
A. Stefani
Seung-Chul Hong
Tae Won Kim
F. Pizza
G. Plazzi
S. Vandi
E. Antelmi
Dimitri Perrin
S. Kuna
P. Schweitzer
C. Kushida
P. Peppard
H. Sørensen
P. Jennum
Emmanuel Mignot
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Papers citing "Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"

21 / 21 papers shown
Title
sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging
Jingyuan Chen
Yuan Yao
Mie Anderson
Natalie Hauglund
Celia Kjaerby
Verena Untiet
Maiken Nedergaard
Jiebo Luo
41
1
0
28 Jan 2025
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models
Tiezhi Wang
Nils Strodthoff
47
5
0
10 Oct 2023
aSAGA: Automatic Sleep Analysis with Gray Areas
aSAGA: Automatic Sleep Analysis with Gray Areas
M. Rusanen
Gabriel Jouan
R. Huttunen
Sami Nikkonen
Sigríður Sigurðardóttir
...
E. S. Arnardóttir
T. Leppänen
A. Islind
S. Kainulainen
H. Korkalainen
24
4
0
03 Oct 2023
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Jathurshan Pradeepkumar
Mithunjha Anandakumar
Vinith Kugathasan
Dhinesh Suntharalingham
S. L. Kappel
A. D. Silva
Chamira U. S. Edussooriya
31
31
0
15 Aug 2022
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable
  Techniques Are Competitive to Deep Learning for Sleep Scoring
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
Jeroen Van Der Donckt
Jonas Van Der Donckt
Emiel Deprost
N. Vandenbussche
Michael Rademaker
Gilles Vandewiele
Sofie Van Hoecke
24
35
0
15 Jul 2022
Classification at the Accuracy Limit -- Facing the Problem of Data
  Ambiguity
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity
C. Metzner
A. Schilling
M. Traxdorf
K. Tziridis
Holger Schulze
P. Krauss
28
11
0
04 Jun 2022
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player
  Generative Adversarial Networks
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks
Zhenyuan Zhang
Tao Shen
Jie M. Zhang
Chao-Xiang Wu
FedML
13
13
0
10 Jan 2022
DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with
  Uncertainty Estimates
DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates
Luigi Fiorillo
Paolo Favaro
F. Faraci
21
79
0
24 Aug 2021
SleepTransformer: Automatic Sleep Staging with Interpretability and
  Uncertainty Quantification
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
Huy P Phan
Kaare B. Mikkelsen
Oliver Y. Chén
P. Koch
Alfred Mertins
M. D. Vos
13
182
0
23 May 2021
MSED: a multi-modal sleep event detection model for clinical sleep
  analysis
MSED: a multi-modal sleep event detection model for clinical sleep analysis
Alexander Neergaard Zahid
P. Jennum
Emmanuel Mignot
H. Sørensen
35
10
0
07 Jan 2021
RobustSleepNet: Transfer learning for automated sleep staging at scale
RobustSleepNet: Transfer learning for automated sleep staging at scale
Antoine Guillot
Valentin Thorey
OOD
40
83
0
07 Jan 2021
The Ultimate DataFlow for Ultimate SuperComputers-on-a-Chip, for
  Scientific Computing, Geo Physics, Complex Mathematics, and Information
  Processing
The Ultimate DataFlow for Ultimate SuperComputers-on-a-Chip, for Scientific Computing, Geo Physics, Complex Mathematics, and Information Processing
V. Milutinovic
Erfan Sadeqi Azer
K. Yoshimoto
Gerhard Klimeck
Miljana Djordjević
...
M. D. Santo
E. Neuhold
Jelena Skoruvcak
L. Dipietro
Ivan Ratković
6
6
0
20 Sep 2020
Automatic detection of microsleep episodes with deep learning
Automatic detection of microsleep episodes with deep learning
A. Malafeev
Anneke Hertig-Godeschalk
David R. Schreier
J. Skorucak
J. Mathis
P. Achermann
19
15
0
07 Sep 2020
U-Time: A Fully Convolutional Network for Time Series Segmentation
  Applied to Sleep Staging
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Mathias Perslev
M. Jensen
S. Darkner
P. Jennum
Christian Igel
AI4TS
25
243
0
24 Oct 2019
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Huy P Phan
Oliver Y. Chén
P. Koch
Zongqing Lu
Ian Mcloughlin
Alfred Mertins
M. D. Vos
26
116
0
30 Jul 2019
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
A. N. Olesen
Stanislas Chambon
Valentin Thorey
P. Jennum
Emmanuel Mignot
H. Sørensen
24
7
0
16 May 2019
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with
  Channel Mismatch
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
Huy P Phan
Oliver Y. Chén
P. Koch
Alfred Mertins
M. D. Vos
25
35
0
11 Apr 2019
DOSED: a deep learning approach to detect multiple sleep micro-events in
  EEG signal
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Stanislas Chambon
Valentin Thorey
P. Arnal
Emmanuel Mignot
Alexandre Gramfort
27
59
0
07 Dec 2018
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for
  Sequence-to-Sequence Automatic Sleep Staging
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
15
414
0
28 Sep 2018
A deep learning architecture to detect events in EEG signals during
  sleep
A deep learning architecture to detect events in EEG signals during sleep
Stanislas Chambon
Valentin Thorey
P. Arnal
Emmanuel Mignot
Alexandre Gramfort
17
41
0
11 Jul 2018
Joint Classification and Prediction CNN Framework for Automatic Sleep
  Stage Classification
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Huy P Phan
Fernando Andreotti
Navin Cooray
Oliver Y. Chén
M. D. Vos
22
337
0
16 May 2018
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