Predicting Sleeping Quality using Convolutional Neural Networks

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), Naïve 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. is applied for stage classification. By using a phase encoding algorithm, the complex-valued non-linear features are obtained and used as the input to Complex-Valued Neural Network (CVNN) for classifying sleep stages. A feature extraction method identified as the statistical behaviour of local extrema is indicated for the sleep stage classification. The statistical features calculated from the segmented EEG-epochs are plotted onto the then the structural the are based with