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Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice.

Donohue KD, Medonza DC, Crane ER, O'Hara BF - Biomed Eng Online (2008)

Bottom Line: A single Polyvinylidine Difluoride (PVDF) sensor on the cage floor generates signals from motion resulting in pressure.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. donohue@engr.uky.edu

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.

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Sensor signal with corresponding sleep-wake decision statistic computed every 2 seconds. Long time-range to observe gross signal behaviour over sleep and active periods with corresponding decision statistics.
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Figure 6: Sensor signal with corresponding sleep-wake decision statistic computed every 2 seconds. Long time-range to observe gross signal behaviour over sleep and active periods with corresponding decision statistics.

Mentions: Figure 6 shows similar signals and statistics as those shown in Fig. 5 except over a shorter interval. The transition from rest (awake with no significant motion) to sleep occurs at about hour 42.506. There is a slight decrease in amplitude for the sleep state, but the main difference over the transition is the regularity (consistent amplitude and stronger periodicity) of the signal. As the breathing starts to become more regular in the quite active (or rest) state and subtle motion and body shifting decrease, the sleep-wake statistics become less negative and even positive for some epochs. This may be another source of discrepancy between the human observation and the classifier, as the observer would indicate the still mouse with eyes open as awake.


Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice.

Donohue KD, Medonza DC, Crane ER, O'Hara BF - Biomed Eng Online (2008)

Sensor signal with corresponding sleep-wake decision statistic computed every 2 seconds. Long time-range to observe gross signal behaviour over sleep and active periods with corresponding decision statistics.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2365952&req=5

Figure 6: Sensor signal with corresponding sleep-wake decision statistic computed every 2 seconds. Long time-range to observe gross signal behaviour over sleep and active periods with corresponding decision statistics.
Mentions: Figure 6 shows similar signals and statistics as those shown in Fig. 5 except over a shorter interval. The transition from rest (awake with no significant motion) to sleep occurs at about hour 42.506. There is a slight decrease in amplitude for the sleep state, but the main difference over the transition is the regularity (consistent amplitude and stronger periodicity) of the signal. As the breathing starts to become more regular in the quite active (or rest) state and subtle motion and body shifting decrease, the sleep-wake statistics become less negative and even positive for some epochs. This may be another source of discrepancy between the human observation and the classifier, as the observer would indicate the still mouse with eyes open as awake.

Bottom Line: A single Polyvinylidine Difluoride (PVDF) sensor on the cage floor generates signals from motion resulting in pressure.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA. donohue@engr.uky.edu

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.

Show MeSH