Abstract
This work presents a non-invasive high-throughput system for automatically detecting characteristic behaviours in mice over extended periods of time, useful for phenotyping experiments. The system classifies time intervals on the order of 2 to 4 seconds as corresponding to motions consistent with either active wake or inactivity associated with sleep. A single Polyvinylidine Difluoride (PVDF) sensor on the cage floor generates signals from motion resulting in pressure. This paper develops a linear classifier based on robust features extracted from normalized power spectra and autocorrelation functions, as well as novel features from the collapsed average (autocorrelation of complex spectrum), which characterize transient and periodic properties of the signal envelope. Performance is analyzed through an experiment comparing results from direct human observation and classification of the different behaviours with an automatic classifier used in conjunction with this system. Experimental results from over 28.5 hours of data from 4 mice indicate a 94% classification rate relative to the human observations. Examples of sequential classifications (2 second increments) over transition regions between sleep and wake behaviour are also presented to demonstrate robust performance to signal variation and explain performance limitations.
Highlights
All mammals, and perhaps all animals, sleep [1]
Classification performance increases on the order of 0.6% to 2% for increased segment length, while a 2% to 3% increase results from the use of compression
This paper presented a sensor and classification system that can be used for high-throughput systems to identify rodent behaviours associated with sleep and wake states
Summary
Perhaps all animals, sleep [1]. Recent estimates suggest that 50 to 70 million Americans experience either chronic or intermittent sleep related problems, and each year sleep disorders add billions to the national health care bill, and many more billions in accidents caused by sleepiness [3,4]. The most accepted technologies currently used in sleep analyses of mammals include. Electroencephalographic (EEG) and Electromyographic (EMG) recordings [5,6]. While these technologies accurately discriminate between the sleep and wake states through semiautomatic scoring of the signal, the required preparations and analyses (surgery, recovery, signal scoring ...) limit its application in large-scale experiments often needed for genetic studies with rodents. For characterizing certain behavioural trends in larger groups, high accuracy on a small scale provided by EEG/ EMG may not be required
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