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.
|Title of host publication||The International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies CCSET2022|
|Publication status||Accepted/In press - 11 Apr 2022|
|Event||International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies - Prince Sultan University, Saudi Arabia|
Duration: 10 May 2022 → 11 May 2022
|Conference||International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies|
|Period||10/05/22 → 11/05/22|