Date Available
4-29-2014
Year of Publication
2014
Degree Name
Master of Science in Electrical Engineering (MSEE)
Document Type
Master's Thesis
College
Engineering
Department/School/Program
Electrical Engineering
First Advisor
Dr. Kevin Donohue
Abstract
While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method.
Recommended Citation
Gooch, Steven R., "A METHOD FOR NON-INVASIVE, AUTOMATED BEHAVIOR CLASSIFICATION IN MICE, USING PIEZOELECTRIC PRESSURE SENSORS" (2014). Theses and Dissertations--Electrical and Computer Engineering. 56.
https://uknowledge.uky.edu/ece_etds/56
