Predicting Sleeping Quality using Convolutional Neural Networks
This work addresses sleep disorder diagnosis by providing a baseline for future research, but it is incremental as it applies an existing CNN method to sleep data.
The paper tackles sleep stage classification by proposing a CNN architecture that improves performance over traditional machine learning methods like Logistic Regression and SVM, achieving reported metrics including accuracy, sensitivity, and F-score on three public datasets.
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.