Sleep-stage scoring in mice: The influence of data pre-processing on a system's performance

Vasiliki-Maria Katsageorgiou, Glenda Lassi, Valter Tucci, Vittorio Murino, Diego Sona

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)
Subtitle of host publicationProceedings of a meeting held 25-29 August 2015, Milan, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages598-601
Number of pages4
ISBN (Electronic)9781424492718
ISBN (Print)9781424492695
DOIs
Publication statusPublished - Dec 2015

Publication series

Name
ISSN (Print)1094-687X
ISSN (Electronic)1558-4615

Keywords

  • Journal Article

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