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), 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.

Automated summary

Identifying sleep stages and patterns is crucial for diagnosing and treating sleep disorders. This paper proposes a CNN architecture to improve the classification performance by benchmarking it against traditional machine learning methods on publicly available sleep datasets. Accuracy, sensitivity, specificity, precision, recall, and F-score are reported as baseline for future research in this direction.