Análisis Dinámico de Relevancia en Bioseñales
Abstract : In this work, a methodology for biosignal analysis (e.g. pathology diagnosis) is discussed, which is based on dynamic relevance analysis of stochastic features extracted from different decomposition techniques of biosignal recordings. Dimension reduction is carried out by adapting in time...
- Autores:
-
Sepúlveda Cano, Lina María
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/19997
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/19997
http://bdigital.unal.edu.co/10221/
- Palabra clave:
- 0 Generalidades / Computer science, information and general works
51 Matemáticas / Mathematics
61 Ciencias médicas; Medicina / Medicine and health
Análisis de bioseñales
procesos estocásticos
sistemas de reconocimiento de configuraciones
biosignal analysis
stochastic processes
Pattern recognition systems
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Abstract : In this work, a methodology for biosignal analysis (e.g. pathology diagnosis) is discussed, which is based on dynamic relevance analysis of stochastic features extracted from different decomposition techniques of biosignal recordings. Dimension reduction is carried out by adapting in time commonly used latent variable techniques, in such a way, that the data information is maximally preserved for a given relevance function. Specifically, since the maximum variance is assumed as a measure of relevance, time– adapted supervised approaches are developed. Additionally, in the case of high dimensionality data with significant correlation among the whole set, a dimensionality reduction technique is proposed, based on time–frequency relevance maps. The proposed approaches are experimentally assessed on real-world data sets, allowing to confirm whether the proposed feature selection algorithm is adequate for classification purposes. The conjunction of these advances conforms a methodology for training pattern recognition systems, which is a fully automatized dimensionality reduction method that allows the use of functional representations. The main advantage of the proposed methodology, is that preserves the maximum information among the high dimensional input data. In this terms of classifi- cation performance, the proposed methodology is efficient and competitive, outperforming other similar methods. |
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