Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements.

Détails

ID Serval
serval:BIB_4253F1EB123C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements.
Périodique
IEEE Transactions on Bio-medical Engineering
Auteur(s)
Flores A.E., Flores J.E., Deshpande H., Picazo J.A., Xie X.S., Franken P., Heller H.C., Grahn D.A., O'Hara B.F.
ISSN
0018-9294[print], 0018-9294[linking]
Statut éditorial
Publié
Date de publication
2007
Peer-reviewed
Oui
Volume
54
Numéro
2
Pages
225-233
Langue
anglais
Résumé
Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs.
Mots-clé
Animals, Artificial Intelligence, Mice, Mice, Inbred AKR, Mice, Inbred C57BL, Mice, Inbred DBA, Movement/physiology, Pattern Recognition, Automated/methods, Polysomnography/methods, Respiratory Mechanics/physiology, Sleep/physiology, Transducers
Pubmed
Web of science
Création de la notice
24/01/2008 16:31
Dernière modification de la notice
03/03/2018 16:37
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