title: Sleep-stage scoring in mice: The influence of data pre-processing on a system's performance.
abstract: Sleep-stage analysis in mice and rats has received growing attention in recent years, due to the fact that mice display electrical activity during sleep which has underlying similarities with that of human sleep. Both conventional manual and automatic sleep-wakefulness scoring are rule based tasks which use brain waves measured by Electroencephalogram (EEG) and activity detected by Electromyography (EMG) of skeletal muscles. Several works have been conducted trying to provide an automatic sleep-scoring system on the basis of machine learning methods. In this study we try to understand the reasons behind the complexity of this problem and we emphasize the importance of normalization procedure that leads to a better stage discrimination comparing different classification methods.
authors.forename: Vasiliki-Maria
authors.initials: VM
authors.lastname: Katsageorgiou
authors.forename: Glenda
authors.initials: G
authors.lastname: Lassi
authors.forename: Valter
authors.initials: V
authors.lastname: Tucci
authors.forename: Vittorio
authors.initials: V
authors.lastname: Murino
authors.forename: Diego
authors.initials: D
authors.lastname: Sona
mesh_terms: D000818:Animals; D004569:Electroencephalography; D004576:Electromyography; D006801:Humans; D051379:Mice; D051381:Rats; D012894:Sleep Stages; D014851:Wakefulness