AUTOMATIC DETECTION OF SLEEP AND WAKE STATES IN MICE USING PIEZOELECTRIC SENSORS

Currently technologies such as EEG, EMG and EOG recordings are the established methods used in the analysis of sleep. But if these methods are to be employed to study sleep in rodents, extensive surgery and recovery is involved which can be both time consuming and costly. This thesis presents and analyzes a cost effective, non-invasive, high throughput system for detecting the sleep and wake patterns in mice using a piezoelectric sensor. This sensor was placed at the bottom of the mice cages to monitor the movements of the mice. The thesis work included the development of the instrumentation and signal acquisition system for recording the signals critical to sleep and wake classification. Classification of the mouse sleep and wake states were studied for a linear classifier and a Neural Network classifier based on 23 features extracted from the Power Spectrum (PS), Generalized Spectrum (GS), and Autocorrelation (AC) functions of short data intervals. The testing of the classifiers was done on two data sets collected from two mice, with each data set having around 5 hours of data. A scoring of the sleep and wake states was also done via human observation to aid in the training of the classifiers. The performances of these two classifiers were analyzed by looking at the misclassification error of a set of test features when run through a classifier trained by a set of training features. The best performing features were selected by first testing each of the 23 features individually in a linear classifier and ranking them according to their misclassification rate. A test was then done on the 10 best individually performing features where they were grouped in all possible combinations of 5 features to determine the feature combinations leading to the lowest error rates in a multi feature classifier. From this test 5 features were eventually chosen to do the classification. It was found that the features related to the signal energy and the spectral peaks in the 3Hz range gave the lowest errors. Error rates as low as 4% and 9% were achieved from a 5-feature linear classifier for the two data sets. The error rates from a 5-feature Neural Network classifier were found to be 6% and 12% respectively for these two data sets

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

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

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

<p><b>Copyright information:</b></p><p>Taken from "Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice"</p><p>http://www.biomedical-engineering-online.com/content/7/1/14</p><p>BioMedical Engineering OnLine 2008;7():14-14.</p><p>Published online 11 Apr 2008</p><p>PMCID:PMC2365952.</p><p></p

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

<p><b>Copyright information:</b></p><p>Taken from "Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice"</p><p>http://www.biomedical-engineering-online.com/content/7/1/14</p><p>BioMedical Engineering OnLine 2008;7():14-14.</p><p>Published online 11 Apr 2008</p><p>PMCID:PMC2365952.</p><p></p