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- W2179673822 abstract "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.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.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.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.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." @default.
- W2179673822 created "2016-06-24" @default.
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- W2179673822 date "2016-01-01" @default.
- W2179673822 modified "2023-09-28" @default.
- W2179673822 title "Automatic artifacts and arousals detection in whole-night sleep EEG recordings" @default.
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- W2179673822 doi "https://doi.org/10.1016/j.jneumeth.2015.11.005" @default.
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