Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models

2014 ◽  
Vol 10 ◽  
pp. 117-127 ◽  
Author(s):  
Tracey A. Camilleri ◽  
Kenneth P. Camilleri ◽  
Simon G. Fabri
Keyword(s):  
Automatic Detection ◽  
Sleep Eeg ◽  
Multiple Models

scholarly journals Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

2020 ◽  
Vol 2 (3) ◽  
pp. 258-272
Author(s):  
Daphne Chylinski ◽  
Franziska Rudzik ◽  
Dorothée Coppieters ‘t Wallant ◽  
Martin Grignard ◽  
Nora Vandeleene ◽  
...  
Keyword(s):  
Visual Detection ◽  
Sleep Stage ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Human Detection ◽  
Sleep Eeg ◽  
Older Individuals ◽  
Time Frequency ◽  
Eeg Recordings ◽  
Research Centres

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

A novel system for automatic detection of K-complexes in sleep EEG

2017 ◽  
Vol 29 (8) ◽  
pp. 137-157 ◽  
Author(s):  
Cüneyt Yücelbaş ◽  
Şule Yücelbaş ◽  
Seral Özşen ◽  
Gülay Tezel ◽  
Serkan Küççüktürk ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Sleep Eeg

The automatic detection of unattended social category information from unfamiliar faces

2014 ◽  
Author(s):  
Douglas Martin ◽  
Rachel Swainson ◽  
Gillian Slessor ◽  
Jacqui Hutchison ◽  
Diana Marosi
Keyword(s):  
Automatic Detection ◽  
Social Category ◽  
Category Information ◽  
Unfamiliar Faces

Evidence of favorable sleep EEG patterns in adolescent male vigorous football players compared to controls

2009 ◽  
Vol 42 (05) ◽  
Author(s):  
S Brand ◽  
A Djurdjevic ◽  
J Beck ◽  
M Gerber ◽  
M Hatzinger ◽  
...  
Keyword(s):  
Sleep Eeg ◽  
Adolescent Male ◽  
Football Players
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