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Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals
This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian Eigenmaps machine learning approach with dynamical s...
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| Publicado no: | Phys Rev E |
|---|---|
| Main Authors: | , , , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4866516/ https://ncbi.nlm.nih.gov/pubmed/27176304 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1103/PhysRevE.93.042218 |
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