Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection

B. Giraldo, C. Arizmendi, E. Romero, R. Alquezar, P. Caminal, S. Benito, D. Ballesteros

Resultado de la investigación: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

11 Citas (Scopus)

Resumen

One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features.

Idioma originalInglés
Título de la publicación alojada28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Páginas2195-2198
Número de páginas4
DOI
EstadoPublicada - 2006
Publicado de forma externa
Evento28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, Estados Unidos
Duración: 30 ago. 20063 sept. 2006

Serie de la publicación

NombreAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (versión impresa)0589-1019

Conferencia

Conferencia28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
País/TerritorioEstados Unidos
CiudadNew York, NY
Período30/08/063/09/06

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