Predicting Sleeping Quality using Convolutional Neural Networks

Sathish, Vidya Rohini Konanur, Woo, Wai Lok and Ho, Edmond (2023) Predicting Sleeping Quality using Convolutional Neural Networks. In: Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies. Engineering Cyber-Physical Systems and Critical Infrastructures (4). Springer, Cham, Switzerland. ISBN 9783031211003, 9783031211850, 9783031211010

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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.

Item Type: Book Section
Additional Information: International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies, CCSET2022; 10-11 May 2022
Uncontrolled Keywords: machine learning (ML), Deep Learning, Convolutional neural network (CNN), sleep stage classification
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Rachel Branson
Date Deposited: 05 May 2022 10:56
Last Modified: 20 Mar 2024 03:31

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