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

Aaron E. Flores, Judith E. Flores, Hrishikesh Deshpande, Jorge A. Picazo, Xinmin Xie, Paul Franken, H. Craig Heller, Dennis A. Grahn, Bruce F. O'Hara

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


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.

Original languageEnglish
Article number6
Pages (from-to)225-233
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number2
StatePublished - Feb 2007

Bibliographical note

Funding Information:
Manuscript received January 21, 2006; revised July 1, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant DA13349, Grant RR017182, and Grant MH067752, in part by a grant from Deltagen, Inc., in part by the Department of Defense under Grant from DoD FA9550-05-1-0464, and in part by the National Science Foundation (NSF) under Functional Genomics Infrastructure Award EPS-0132295. Asterisk indicates corresponding author.


  • Activity
  • Automated
  • Behavior
  • Classification
  • Instrumentation
  • Mice
  • Noninvasive
  • Respiration
  • Wake

ASJC Scopus subject areas

  • Biomedical Engineering


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