2015
DOI: 10.5665/sleep.4682
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Unsupervised Online Classifier in Sleep Scoring for Sleep Deprivation Studies

Abstract: Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method.

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Cited by 37 publications
(36 citation statements)
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“…Mice were submitted to CSF using a shaker apparatus (PVC cylinder, diameter 30 cm, height 45 cm; Viewpoint, France) that prevents sleep by transient vertical movements (10–20 ms, height 1 cm) 66 . This system allows sleep disruptions to be achieved without forced locomotion or ambulation.…”
Section: Methodsmentioning
confidence: 99%
“…Mice were submitted to CSF using a shaker apparatus (PVC cylinder, diameter 30 cm, height 45 cm; Viewpoint, France) that prevents sleep by transient vertical movements (10–20 ms, height 1 cm) 66 . This system allows sleep disruptions to be achieved without forced locomotion or ambulation.…”
Section: Methodsmentioning
confidence: 99%
“…We applied the same criteria as used in (Libourel et al, 2015) to evaluate the quality of the 490 agreement : 491…”
Section: Evaluation Of Overlap Of Scoring Methods 480mentioning
confidence: 99%
“…Sleep scoring methods are based on the assumption that the information about sleep states is 49 contained in the recorded signal and can be used as a marker (Libourel et al, 2015). In order to 50 implement a reliable sleep scoring, the candidate marker of sleep and wake must not only show 51 a strong average difference between the two states but this change must be systematic and 52 sustained throughout each state, with a clear separation between the values in each state.…”
Section: Introduction 25mentioning
confidence: 99%
“…However, the expediency and objectivity value of these methods is diminished for those that require a subset of manually scored samples from individual animals to train the algorithm (1)(2)(3)(4). Unsupervised, automated scoring algorithms have addressed these limitations of machine learning with varying degrees of accuracy (72-96% agreement) compared to standard manual scoring (5)(6)(7)(8). Nevertheless, all EEG/EMG based systems of arousal state monitoring are hindered by the time required for surgery, recovery, and in many cases, the repeated-measures experimental designs used for studies in which a small number of animals are used.…”
Section: Introductionmentioning
confidence: 99%