Predicting Sleeping Quality using Convolutional Neural Networks

Vidya Rohini Konanur Sathish, Wai Lok Woo, Edmond S. L. Ho*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationThe International conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies CCSET2022
Publication statusAccepted/In press - 11 Apr 2022
EventInternational conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies - Prince Sultan University, Saudi Arabia
Duration: 10 May 202211 May 2022

Conference

ConferenceInternational conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies
Abbreviated titleCCSET2022
Country/TerritorySaudi Arabia
Period10/05/2211/05/22

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