The combination of self-organizing feature maps and support vector regression for solving the inverse ECG problem

DOI10.1016/j.camwa.2013.09.010zbMath1381.92056OpenAlexW2762756691MaRDI QIDQ316328

Feng Liu, Shanshan Jiang, Wenqing Huang, Ling Xia, Mingfeng Jiang, Ya-Ming Wang

Publication date: 27 September 2016

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2013.09.010

zbMATH Keywords

self-organizing feature mapsupport vector regressioninverse ECG problemtransmembrane potentials

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Biomedical imaging and signal processing (92C55)

Uses Software

• LIBSVM
• GPUSVM

Cites Work

• GPUSVM: a comprehensive CUDA based support vector machine package
• A hybrid model of Maximum Margin Clustering method and Support Vector Regression for noninvasive electrocardiographic imaging
• On the possibility for computing the transmembrane potential in the heart with a one shot method: an inverse problem
• 10.1162/153244302760185252
• Self-organizing maps.

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