A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity

Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using sever...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 69; H. 12; S. 3601 - 3611
Hauptverfasser:	Hossain, Md-Billal, Posada-Quintero, Hugo F., Chon, Ki H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms autoencoder Biomedical monitoring Biomedical signal processing Central nervous system Circuits Convolutional neural networks Datasets Electrodermal activity Galvanic Skin Response Hyperoxia Motion Motion artifacts Neural Networks, Computer Noise reduction Signal-To-Noise Ratio Toxicity White noise
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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