Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks

Objective: We propose a new analytic framework, "Artificial Synthetic Imaging Data (ASID) Workflow," for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permits real-time multi-modal physiological mo...

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Bibliographic Details
Published in:	IEEE journal of biomedical and health informatics Vol. 27; no. 1; pp. 421 - 432
Main Authors:	Shi, Lan, Wank, Marianthie, Chen, Yan, Wang, Yibo, Liu, Yachuan, Hector, Emily C., Song, Peter X.K.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accelerometers Algorithms Artificial neural networks Artificial synthetic imaging data Biomedical monitoring Classification Classification algorithms Computer applications convolutional neural network Deep learning Diagnostic Imaging Heart rate Humans Image classification Long short-term memory Machine Learning Monitoring Neural networks Neural Networks, Computer physiological signals Physiology Random Forest Signal resolution Skin temperature Sleep sleep classification Supervised learning Support vector machines wearable Wearable computers Wearable technology Workflow
ISSN:	2168-2194, 2168-2208, 2168-2208
Online Access:	Get full text
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