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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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Example of piezoelectric signals corresponding to sleep from 2 different mice showing quasi-periodicity with (a) High-amplitude and (b) Low-amplitude.
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Figure 2: Example of piezoelectric signals corresponding to sleep from 2 different mice showing quasi-periodicity with (a) High-amplitude and (b) Low-amplitude.

Mentions: Illustrative examples of sensor signals for different mouse behaviours are shown in Figs. 2 and 3. Figure 2 shows sleep behaviour signals for 2 recordings over separate sensors and amplifiers. The most obvious similarity is the quasi-periodic breathing signal (with periods between 0.3 and 0.4 seconds). The differences include amplitude/scale (due to a combination of the mouse size, sleep position, and amplifier gain) and shape of the periodic waveform. Examples of signals corresponding to wake behaviour are shown in Fig. 3. Figure 3a corresponds to a still mouse (quite wake) with eyes open. In this case the signal amplitudes are on the order of those of the sleep signal. This behaviour often precedes sleep, but typically differs in that the breathing pattern is not as regular as in the case of typical sleep signals with greater envelope variations. The frequency for quite rest often overlaps the breathing from sleep making it difficult to separate sleep from wake behaviours based on the pressure signals alone. However, other studies as well as our own observations indicate that mice are in this state only about 5% of the time [10]. Figure 3b corresponds to an active mouse moving across the cage (large amplitude spikes correspond to the feet striking the PVDF sensor). Signal characteristics corresponding to the typical wake behaviours can be described as random with strong transients (short-time, high amplitude). This is in contrast to the typical sleep behaviour patterns with consistent amplitudes and periodic variations.


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)

Example of piezoelectric signals corresponding to sleep from 2 different mice showing quasi-periodicity with (a) High-amplitude and (b) Low-amplitude.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Example of piezoelectric signals corresponding to sleep from 2 different mice showing quasi-periodicity with (a) High-amplitude and (b) Low-amplitude.
Mentions: Illustrative examples of sensor signals for different mouse behaviours are shown in Figs. 2 and 3. Figure 2 shows sleep behaviour signals for 2 recordings over separate sensors and amplifiers. The most obvious similarity is the quasi-periodic breathing signal (with periods between 0.3 and 0.4 seconds). The differences include amplitude/scale (due to a combination of the mouse size, sleep position, and amplifier gain) and shape of the periodic waveform. Examples of signals corresponding to wake behaviour are shown in Fig. 3. Figure 3a corresponds to a still mouse (quite wake) with eyes open. In this case the signal amplitudes are on the order of those of the sleep signal. This behaviour often precedes sleep, but typically differs in that the breathing pattern is not as regular as in the case of typical sleep signals with greater envelope variations. The frequency for quite rest often overlaps the breathing from sleep making it difficult to separate sleep from wake behaviours based on the pressure signals alone. However, other studies as well as our own observations indicate that mice are in this state only about 5% of the time [10]. Figure 3b corresponds to an active mouse moving across the cage (large amplitude spikes correspond to the feet striking the PVDF sensor). Signal characteristics corresponding to the typical wake behaviours can be described as random with strong transients (short-time, high amplitude). This is in contrast to the typical sleep behaviour patterns with consistent amplitudes and periodic variations.

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