Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its hig...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 112; no. 1; pp. 189 - 199
Main Authors:	Martínez-Zarzuela, Mario, Gómez, Carlos, Díaz-Pernas, Francisco Javier, Fernández, Alberto, Hornero, Roberto
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ireland Ltd 01.10.2013 Elsevier
Subjects:	Adult Aged Algorithms Applied sciences Biological and medical sciences Channels Child Computation Computer Graphics Computer science; control theory; systems Cross Approximate Entropy CUDA Entropy Exact sciences and technology Female GPGPU Humans Internal Medicine Investigative techniques, diagnostic techniques (general aspects) Language processing and microprogramming Magnetoencephalography Magnetoencephalography - statistics & numerical data Male Medical sciences Microprocessors Middle Aged Multidimensional data Nervous system Neural signal analysis Other Radiodiagnosis. Nmr imagery. Nmr spectrometry Running Signal processing Signal Processing, Computer-Assisted Software Statistical methods CUDA Neural signal analysis Cross Approximate Entropy Magnetoencephalography GPGPU Parallel algorithm Costs Statistical analysis Processor Probabilistic approach Similarity Time series Electroencephalography Entropy Biomedical data processing Graphic processing unit Signal processing Parallel computation Signal analysis Central unit Accelerator Biomedical engineering
ISSN:	0169-2607, 1872-7565, 1872-7565
Online Access:	Get full text
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