Automatic artifacts and arousals detection in whole-night sleep EEG recordings
D Coppieters't Wallant, V Muto, G Gaggioni… - Journal of neuroscience …, 2016 - Elsevier
D Coppieters't Wallant, V Muto, G Gaggioni, M Jaspar, SL Chellappa, C Meyer…
Journal of neuroscience methods, 2016•ElsevierBackground In sleep electroencephalographic (EEG) signals, artifacts and arousals marking
are usually part of the processing. This visual inspection by a human expert has two main
drawbacks: it is very time consuming and subjective. New method To detect artifacts and
arousals in a reliable, systematic and reproducible automatic way, we developed an
automatic detection based on time and frequency analysis with adapted thresholds derived
from data themselves. Results The automatic detection performance is assessed using 5 …
are usually part of the processing. This visual inspection by a human expert has two main
drawbacks: it is very time consuming and subjective. New method To detect artifacts and
arousals in a reliable, systematic and reproducible automatic way, we developed an
automatic detection based on time and frequency analysis with adapted thresholds derived
from data themselves. Results The automatic detection performance is assessed using 5 …
Background
In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time consuming and subjective.
New method
To detect artifacts and arousals in a reliable, systematic and reproducible automatic way, we developed an automatic detection based on time and frequency analysis with adapted thresholds derived from data themselves.
Results
The automatic detection performance is assessed using 5 statistic parameters, on 60 whole night sleep recordings coming from 35 healthy volunteers (male and female) aged between 19 and 26. The proposed approach proves its robustness against inter- and intra-, subjects and raters’ scorings, variability. The agreement with human raters is rated overall from substantial to excellent and provides a significantly more reliable method than between human raters.
Comparison
Existing methods detect only specific artifacts or only arousals, and/or these methods are validated on short episodes of sleep recordings, making it difficult to compare with our whole night results.
Conclusion
The method works on a whole night recording and is fully automatic, reproducible, and reliable. Furthermore the implementation of the method will be made available online as open source code.
Elsevier
