Convex optimisation-based methods for K-complex detection

DOI10.1016/j.amc.2015.07.005zbMath1410.94024OpenAlexW1041725261MaRDI QIDQ668213

Juan-Miguel Gracia

Publication date: 18 March 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2015.07.005

zbMATH Keywords

feature extractiondata analysisconvex optimisationbiological signal classificationsignal approximation

Mathematics Subject Classification ID

Convex programming (90C25) Applications of mathematical programming (90C90) Numerical optimization and variational techniques (65K10) Biomedical imaging and signal processing (92C55) Detection theory in information and communication theory (94A13)

Related Items (3)

Chebyshev approximation by linear combinations of fixed knot polynomial splines with weighting functions ⋮ Generalised rational approximation and its application to improve deep learning classifiers ⋮ Linear least squares problems involving fixed knots polynomial splines and their singularity study

Uses Software

• SDPT3
• CVX
• WEKA

Cites Work

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• Linear least squares problems involving fixed knots polynomial splines and their singularity study
• On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0
• DETECTING K-COMPLEXES FOR SLEEP STAGE IDENTIFICATION USING NONSMOOTH OPTIMIZATION
• Graph Implementations for Nonsmooth Convex Programs

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