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

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 12; pp. 3601 - 3611
• Main Authors: Hossain, Md-Billal, Posada-Quintero, Hugo F., Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...