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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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2023-01, Vol.27 (1), p.421-432
Hauptverfasser:	Shi, Lan, Wank, Marianthie, Chen, Yan, Wang, Yibo, Liu, Yachuan, Hector, Emily C., Song, Peter X.K.
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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