Comparison of support-vector machine and sparse representation using a modified rule-based method for automated myocardial ischemia detection

DOI10.1155/2016/9460375zbMath1423.92182OpenAlexW2273248468WikidataQ36559172 ScholiaQ36559172MaRDI QIDQ306029

Keng-Sheng Lin, Yi-Li Tseng, Fu-Shan Jaw

Publication date: 31 August 2016

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/9460375

zbMATH Keywords

ECGmyocardial ischemia detectionrepresentation-based classificationsupport-vector machines

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Computational methods for problems pertaining to biology (92-08)

Related Items (1)

Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features

Uses Software

• LIBSVM
• PhysioToolkit

Cites Work

• Computational problems in engineering
• Graph Implementations for Nonsmooth Convex Programs
• Analysis of Sparse Representation and Blind Source Separation
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution

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