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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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Quad cage and sensor system (a) top view showing cage walls on top of sensors on base (b) side view showing sensor layers on cage floor and connection to sensor amplifier.
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Figure 1: Quad cage and sensor system (a) top view showing cage walls on top of sensors on base (b) side view showing sensor layers on cage floor and connection to sensor amplifier.

Mentions: A four-cage unit housed each mouse under test in individual compartments with sensors to capture pressure changes from anywhere on the cage floor. Figure 1 shows the top and side views of the cages with the sensing system. The cage was constructed from Lexan (polycarbonate). Four separate walled compartments with attached food/water structures with open bottoms were designed such that they can be inserted on the base and hold the sensor pad in place. Each PVDF sensing transducer covered the cage floor and extended 1.27 cm beyond the cage walls so the mice did not have access to the edges as shown in Fig. 1. The PVDF sensor was 17.78 cm by 17.78 cm square and consisted of a 110 μm thick dielectric, made by Measurement Specialties, Inc (Hampton, VA). Silver ink is sputtered on each side of the PVDF creating a conductive link from any position where pressure is applied. A protective plastic sheet (50.8 μm thick) was placed over the sensors to protect it from moisture and allow for easy cleaning. Additional bedding was placed on top of the plastic sheet for the animal's comfort. A 1.6 mm thick rubber pad made of Shore A 70 durometer silicon was placed between the sensor and the base to attenuate crosstalk from the pressure signals to other sensor pads. The side view in Fig. 1b shows the sensor placement with adjacent layers between the base and the cage walls. The chamber below the cage floors was 10 cm in height, and housed the instrumentation amplifiers for the sensors.


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)

Quad cage and sensor system (a) top view showing cage walls on top of sensors on base (b) side view showing sensor layers on cage floor and connection to sensor amplifier.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Quad cage and sensor system (a) top view showing cage walls on top of sensors on base (b) side view showing sensor layers on cage floor and connection to sensor amplifier.
Mentions: A four-cage unit housed each mouse under test in individual compartments with sensors to capture pressure changes from anywhere on the cage floor. Figure 1 shows the top and side views of the cages with the sensing system. The cage was constructed from Lexan (polycarbonate). Four separate walled compartments with attached food/water structures with open bottoms were designed such that they can be inserted on the base and hold the sensor pad in place. Each PVDF sensing transducer covered the cage floor and extended 1.27 cm beyond the cage walls so the mice did not have access to the edges as shown in Fig. 1. The PVDF sensor was 17.78 cm by 17.78 cm square and consisted of a 110 μm thick dielectric, made by Measurement Specialties, Inc (Hampton, VA). Silver ink is sputtered on each side of the PVDF creating a conductive link from any position where pressure is applied. A protective plastic sheet (50.8 μm thick) was placed over the sensors to protect it from moisture and allow for easy cleaning. Additional bedding was placed on top of the plastic sheet for the animal's comfort. A 1.6 mm thick rubber pad made of Shore A 70 durometer silicon was placed between the sensor and the base to attenuate crosstalk from the pressure signals to other sensor pads. The side view in Fig. 1b shows the sensor placement with adjacent layers between the base and the cage walls. The chamber below the cage floors was 10 cm in height, and housed the instrumentation amplifiers for the sensors.

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