Novel Algorithm to Classify Sleep Stages

Son, Pham Nguyen; Son, Nguyen Dinh; Phuong, Hoang Minh; Van, Nguyen Thi Phuoc; Minh, Nguyễn Đức

doi:10.1109/icst46873.2019.9047717

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The collection of data by numerous healthcare sensors assisted the Artificial Intelligence (AI) systems in predicting and analysing various types of health-related issues [1]. Such input data can be used as input for Machine Learning (ML) algorithms to further analyze the patients' health status automatically [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting Sleeping Quality Using Convolutional Neural Networks

Konanur

Woo²,

Ho³

2023

Engineering Cyber-Physical Systems and Critical Infrastructures

View full text Add to dashboard Cite

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), Naïve 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.

show abstract

Section: Introductionmentioning

confidence: 99%