RT info:eu-repo/semantics/article
T1 Feature Extraction of Galvanic Skin Responses by Nonnegative Sparse Deconvolution
A1 Hernando Gallego, Francisco
A1 Luengo García, David
A1 Artés Rodríguez, Antonio
K1 Matemática aplicada
K1 Biotecnología
K1 Ingeniería médica
K1 Psicofisiología
K1 Actividad electrodérmica (EDA)
K1 Deconvolución no negativa
K1 Respuesta galvánica de la piel (GSR)
K1 Aproximación dispersa
K1 Sistema nervioso simpático (SNS)
K1 Sensores portátiles
K1 12 Matemáticas
K1 1203 Ciencia de Los Ordenadores
K1 2406 Biofísica
K1 6106.10 Psicología Fisiológica
AB Wearable sensors are increasingly taking part in daily activities, not only because of the recent society health concern, but also due to their relevance in the medical industry. In this paper, a galvanic skin response (GSR) extraction technique has been developed in order to interpret electrodermal activity (EDA) records, which can be useful both for ambulatory and health applications. The core of the proposed approach is a novel feature extraction scheme that is based on a nonnegative sparse deconvolution of the observed GSR signals. Unlike previous approaches, the resulting SparsEDA algorithm is fast (immediately extracting the skin conductance level and response), efficient (being able to work with any sampling rate and signal length), and highly interpretable (due to the sparsity of the extracted phasic component of the GSR). Results on real data from 100 different subjects confirm the good performance of the method, which has been released through a free web-based code repository.
PB IEEE Institute of Electrical and Electronics Engineers
SN 2168-2194
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/83815
UL https://uvadoc.uva.es/handle/10324/83815
LA eng
NO IEEE Journal of Biomedical and Health Informatics, 2018, vol. 22, n. 5, p. 1385-1394.
NO Producción Científica
DS UVaDOC
RD 25-mar-2026