Year of Publication

2014

Degree Name

Master of Science in Electrical Engineering (MSEE)

Document Type

Master's Thesis

College

Engineering

Department

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.

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