TY - GEN
T1 - Predicting Sleeping Quality using Convolutional Neural Networks
AU - Konanur Sathish, Vidya Rohini
AU - Woo, Wai Lok
AU - Ho, Edmond S. L.
PY - 2023/3/12
Y1 - 2023/3/12
N2 - 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.
AB - 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.
KW - machine learning (ML)
KW - Deep Learning
KW - Convolutional neural network (CNN)
KW - sleep stage classification
U2 - 10.1007/978-3-031-21101-0_14
DO - 10.1007/978-3-031-21101-0_14
M3 - Conference contribution
SN - 9783031211003
T3 - Engineering Cyber-Physical Systems and Critical Infrastructures (ECPSCI)
SP - 175
EP - 184
BT - Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies
A2 - , Ahmed A. Abd El-Latif
A2 - , Yassine Maleh
A2 - , Wojciech Mazurczyk
A2 - , Mohammed A. El-Affendi
A2 - , Mohamed I. Alkanhal
PB - Springer
CY - Cham, Switzerland
T2 - International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies
Y2 - 10 May 2022 through 11 May 2022
ER -