Emmanuel Mignot

Orcid: 0000-0002-6928-5310

According to our database1, Emmanuel Mignot authored at least 28 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
SViT: A Spectral Vision Transformer for the Detection of REM Sleep Behavior Disorder.
IEEE J. Biomed. Health Informatics, September, 2023

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis.
IEEE Trans. Biomed. Eng., September, 2023

RRWaveNet: A Compact End-to-End Multiscale Residual CNN for Robust PPG Respiratory Rate Estimation.
IEEE Internet Things J., September, 2023

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.
IEEE Trans. Biomed. Eng., 2023

Automatic Detection of Chronic Insomnia from Polysomnographic and Clinical Variables Using Machine Learning.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

2022
Age estimation from sleep studies using deep learning predicts life expectancy.
npj Digit. Medicine, 2022

RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation.
CoRR, 2022

Detection of Cheyne-Stokes Breathing using a transformer-based neural network.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

2021
Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.
IEEE J. Biomed. Health Informatics, 2021

MSED: a multi-modal sleep event detection model for clinical sleep analysis.
CoRR, 2021

Polysomnographic Plethysmography Excursions are Reduced in Obese Elderly Men.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

Automatic Segmentation to Cluster Patterns of Breathing in Sleep Apnea.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

Upper Airway Classification in Sleep Endoscopy Examinations using Convolutional Recurrent Neural Networks<sup>*</sup>.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting.
CoRR, 2020

Deep transfer learning for improving single-EEG arousal detection.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

Predicting Age with Deep Neural Networks from Polysomnograms.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019
Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph.
F1000Research, 2019

Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness.
CoRR, 2019

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal.
CoRR, 2018

A Deep Learning Architecture to Detect Events in EEG Signals During Sleep.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

2017
The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy.
CoRR, 2017

2015
SEV - a software toolbox for large scale analysis and visualization of polysomnography data.
Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2015

2014
Exploring medical diagnostic performance using interactive, multi-parameter sourced receiver operating characteristic scatter plots.
Comput. Biol. Medicine, 2014


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