  Identifying sleep stages and patterns is an essential part of diagnosing and
treating sleep disorders. With the advancement of smart technologies, sensor
data related to sleeping patterns can be captured easily. In this paper, we
propose a Convolution Neural Network (CNN) architecture that improves the
classification performance. In particular, we benchmark the classification
performance from different methods, including traditional machine learning
methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest
Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3
publicly available sleep datasets. The accuracy, sensitivity, specificity,
precision, recall, and F-score are reported and will serve as a baseline to
simulate the research in this direction in the future.
