  Sleep studies are imperative to recapitulate phenotypes associated with sleep
loss and uncover mechanisms contributing to psychopathology. Most often,
investigators manually classify the polysomnography into vigilance states,
which is time-consuming, requires extensive training, and is prone to
inter-scorer variability. While many works have successfully developed
automated vigilance state classifiers based on multiple EEG channels, we aim to
produce an automated and open-access classifier that can reliably predict
vigilance state based on a single cortical electroencephalogram (EEG) from
rodents to minimize the disadvantages that accompany tethering small animals
via wires to computer programs. Approximately 427 hours of continuously
monitored EEG, electromyogram (EMG), and activity were labeled by a domain
expert out of 571 hours of total data. Here we evaluate the performance of
various machine learning techniques on classifying 10-second epochs into one of
three discrete classes: paradoxical, slow-wave, or wake. Our investigations
include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic
Regression Classifiers, and Artificial Neural Networks. These methodologies
have achieved accuracies ranging from approximately 74% to approximately 96%.
Most notably, the Random Forest and the ANN achieved remarkable accuracies of
95.78% and 93.31%, respectively. Here we have shown the potential of various
machine learning classifiers to automatically, accurately, and reliably
classify vigilance states based on a single EEG reading and a single EMG
reading.
