Filters








55 Hits in 4.0 sec

Automatic detection of microsleep episodes with deep learning [article]

Alexander Malafeev, Anneke Hertig-Godeschalk, David R. Schreier, Jelena Skorucak, Johannes Mathis, Peter Achermann
2021 arXiv   pre-print
We aimed for automatic detection of MSEs with machine learning, i.e. with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients.  ...  We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts.  ...  As a result of this work, we provide a proof of principle that reliable automatic MSE detection with deep neuronal networks working with raw EEG and EOG data as input is feasible with a quality close to  ... 
arXiv:2009.03027v2 fatcat:jrselwxiovdnbdfcn26oielmda

Automatic Detection of Microsleep Episodes With Deep Learning

Alexander Malafeev, Anneke Hertig-Godeschalk, David R. Schreier, Jelena Skorucak, Johannes Mathis, Peter Achermann
2021 Frontiers in Neuroscience  
We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients.  ...  We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts.  ...  and JM brought in longstanding expertise with microsleep episodes in patients, collected the data and performed visual scoring.  ... 
doi:10.3389/fnins.2021.564098 pmid:33841068 pmcid:PMC8024556 fatcat:z6ijlxngc5gghlksqom5k3l3e4

Automatic Detection of Microsleep Episodes With Deep Learning

Alexander Malafeev, Anneke Hertig-Godeschalk, David R Schreier, Jelena Skorucak, Johannes Mathis, Peter Achermann
2021
We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients.  ...  We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to tha [...]  ...  As a result of this work, we provide a proof of principle that reliable automatic MSE detection with deep neuronal networks working with raw EEG and EOG data as input is feasible with a quality close to  ... 
doi:10.48350/157372 fatcat:tinx7ks2izcirlapydd627ekjy

Automatic Detection of Microsleep Episodes With Deep Learning

Alexander Malafeev, Anneke Hertig-Godeschalk, David R Schreier, Jelena Skorucak, Johannes Mathis, Peter Achermann
2021
We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients.  ...  We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts.  ...  As a result of this work, we provide a proof of principle that reliable automatic MSE detection with deep neuronal networks working with raw EEG and EOG data as input is feasible with a quality close to  ... 
doi:10.5167/uzh-215950 fatcat:dhxjv2rapbgvbiu6i2qtgrghdq

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment

Jelena Skorucak, Anneke Hertig-Godeschalk, Peter Achermann, Johannes Mathis, David R. Schreier
2020 Frontiers in Neuroscience  
Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes.  ...  Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants.  ...  JS and AH-G performed the data analysis and wrote the first draft of the manuscript.  ... 
doi:10.3389/fnins.2020.00008 pmid:32038155 pmcid:PMC6990913 fatcat:7g6ana772ffefg3ima2bgglp44

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes

Chun Siong Soon, Ksenia Vinogradova, Ju Lynn Ong, Vince D Calhoun, Thomas Liu, Zhou Juan Helen, Kwun Kei Ng, Michael W L Chee
2021 NeuroImage  
By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread  ...  Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions.  ...  The study was funded by the grants NMRC/STaR/0015/2013 and STaR19may-001 provided by the National Medical Research Council (Ministry of Health, Singapore).  ... 
doi:10.1016/j.neuroimage.2021.118129 pmid:33951513 fatcat:fdi6rgm4ljhwbc2yrqncnathyq

EEG-Based Lapse Detection With High Temporal Resolution

P.R. Davidson, R.D. Jones, M.T.R. Peiris
2007 IEEE Transactions on Biomedical Engineering  
We have developed a system capable of detecting lapses in real-time with second-scale temporal resolution.  ...  Using estimates of EEG log-power spectra from up to 4 s prior to a lapse improved detection compared with only using the most recent estimate.  ...  These episodes are often termed lapses or microsleeps [5] , and indicate temporary deactivation of the cortical networks responsible for task performance [6] .  ... 
doi:10.1109/tbme.2007.893452 pmid:17518279 fatcat:my5jo4oxkfffvo5h2kuc6gmgsu

A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications [article]

Lukas Wolf, Ard Kastrati, Martyna Beata Płomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer
2022 arXiv   pre-print
Our end-to-end deep learning-based framework brings recent advances in Computer Vision to the forefront of the times series segmentation of EEG data.  ...  DETRtime achieves state-of-the-art performance in ocular event detection across diverse eye-tracking experiment paradigms.  ...  The proposed algorithm detects microsleep episodes in eye movement data, showing the high importance of the tool.  ... 
arXiv:2206.08672v1 fatcat:tzukgtvpv5ephncct4orvbxhhm

Detecting of Fatigue States of a Car Driver [chapter]

Roman Bittner, Karel Hána, Lubomír Poušek, Pavel Smrka, Petr Schreib, Petr Vysoký
2000 Lecture Notes in Computer Science  
This paper deals with research on fatigue states of car drivers on freeways and similar roads. All experiments are performed on-the-road.  ...  The results of experiments already performed indicate that the values of symptoms evolve in cycles, which is essential to take into account during design of classifiers in the future.  ...  Spectral EEG analysis is the most appropriate method for detecting the onset and different stages of sleep -but automatic evaluation of drowsiness via EEG analysis failed.  ... 
doi:10.1007/3-540-39949-6_32 fatcat:rmyfh7zbw5b5nkpixv4zukqlau

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment

Jelena Skorucak, Anneke Hertig-Godeschalk, Peter Achermann, Johannes Mathis, David R Schreier
2020
Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes.  ...  Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes.  ...  JS and AH-G performed the data analysis and wrote the first draft of the manuscript.  ... 
doi:10.5167/uzh-195695 fatcat:gaflv7iaavddhielfgdixaspke

Microdream neurophenomenology

Tore Nielsen
2017 Neuroscience of Consciousness  
But in the laboratory these transitions afford a unique view of how experience is transformed from the perceptually grounded consciousness of wakefulness to the hallucinatory simulations of dreaming.  ...  Nightly transitions into sleep are usually uneventful and transpire in the blink of an eye.  ...  Subjects commonly-sometimes adamantly-deny having slept during microsleep episodes despite clear electrophysiological (e.g.  ... 
doi:10.1093/nc/nix001 pmid:30042836 pmcid:PMC6007184 fatcat:56dn3xo3qzadhjhzl3ynqrck6m

A machine learning framework for space medicine predictive diagnostics with physiological signals

Ning Wang, Michael R. Lyu, Chenguang Yang
2013 2013 IEEE Aerospace Conference  
Prognostics and health management (PHM) in the context of space missions focuses on the fundamental issues of system failures in an attempt to predict when the failures may occur, and links these issues  ...  Considering the inherent risks of space missions and the difficulty of direct communications between crew and ground support medical specialists, we see that greater autonomy in medical operations for  ...  Studies have reported episodes of fatigue and the occurrence of uncontrolled sleep periods (microsleeps) in pilots [3] .  ... 
doi:10.1109/aero.2013.6497431 fatcat:cwmboivdrjhf3cioakc2jssvtu

Experimental sleep deprivation as a tool to test memory deficits in rodents

Valeria Colavito, Paolo F. Fabene, Gigliola Grassi-Zucconi, Fabien Pifferi, Yves Lamberty, Marina Bentivoglio, Giuseppe Bertini
2013 Frontiers in Systems Neuroscience  
Studies that have accurately described methodological aspects of the SD protocol are first reviewed, followed by procedures to investigate SD-induced impairment of learning and memory consolidation in  ...  The vast majority of these studies have been aimed at understanding the contribution of sleep to cognition, and in particular to memory.  ...  For example, SD can never be truly "total" for extended periods of time, as episodes of "microsleep" (short episodes of intrusion of sleep into wakefulness lasting as little as a few seconds) become inevitable  ... 
doi:10.3389/fnsys.2013.00106 pmid:24379759 pmcid:PMC3861693 fatcat:eqcuucf3fvdlri7qjhnkhyuie4

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe  ...  Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others.  ...  This work was funded by the Ministry of Science, Research and Arts of Baden-Württemberg (MWK) as part of the project Q-AMeLiA (Quality Assurance of Machine Learning Applications).  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

Prediction of microsleeps using EEG inter-channel relationships [article]

Abdul Baseer Buriro, University Of Canterbury
2019
The aim of this study was to explore various inter-channel relationships in the electroencephalogram (EEG) for detection/prediction of microsleeps.  ...  Such episodes of unresponsiveness are of particularly high importance in people who perform high-risk and monotonous activities requiring extended- attention and unimpaired visuomotor performance, such  ...  The conjunction of a non-tracking episode and a video rating of deep-drowsy or lapse was labeled as microsleep.  ... 
doi:10.26021/1844 fatcat:5xswevuve5akdfs7uxssqxob54
« Previous Showing results 1 — 15 out of 55 results