Feature extraction of galvanic skin responses by nonnegative sparse deconvolution

F. Hernando-Galego; David Luengo; Antonio Artes-Rodriguez

doi:10.1109/JBHI.2017.2780252

Publication record · 2017

Feature extraction of galvanic skin responses by nonnegative sparse deconvolution

Authors: F. Hernando-Galego · David Luengo · Antonio Artes-Rodriguez
Year: 2017
Publisher: IEEE
Journal or publication: IEEE Journal of Biomedical and Health Informatics
Google Scholar citations: 112

Journal indicators

CiteScore 2025 (Scopus)14.3

Best quartileQ1

Top 10% in any categoryYes

Positions by category

Electrical and Electronic Engineering48/1030 · Q1 · percentile 95 · Top 10%
Health Informatics9/168 · Q1 · percentile 94 · Top 10%
Computer Science Applications69/1022 · Q1 · percentile 93 · Top 10%
Health Information Management8/64 · Q1 · percentile 88

Scopus source ↗

JCR (Clarivate): Not verified: the supplied ranking file is Scopus CiteScore, not JCR.

Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).

The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.

Research summary

A nonnegative sparse-deconvolution method extracts interpretable features from galvanic skin response signals, separating electrodermal components for ambulatory monitoring, stress assessment and health applications.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.1109/JBHI.2017.2780252 ↗ · View Google Scholar record ↗