Predication of premature neonates prognosis based on their electroencephalogram using artificial neural network
| Titre | Predication of premature neonates prognosis based on their electroencephalogram using artificial neural network |
| Type de publication | Communications avec actes |
| Année de publication | 2015 |
| Titre de la Conférence/colloque | 2015 SAI Intelligent Systems Conference (IntelliSys) |
| Pagination | 527 - 531 |
| Dates du congrès, colloque | 10/11/2015 |
| Langue | Anglais |
| Auteur(s) | Hajjar, Y. A., Hajjar A E S., Daya B. et Chauvet P. |
| Complément de titre | 2015 SAI Intelligent Systems Conference (IntelliSys) |
| Ville, Pays | London, UK |
| Mots-clés | ANN, Artificial neural network, Artificial neural network ANN, Artificial neural networks, Brain, EEG records, EEG signal, EEGDiag, electrical activity, electroencephalogram, Electroencephalography, Intelligent systems, Inter-Burst interval IBI, inter-burst intervals, Java application, medical signal processing, neural nets, Pediatrics, Prediction, premature neonates prognosis, Prognostics and health management, Receivers, Training |
| Résumé | The electroencephalogram (EEG) is a signal that measures the electrical activity of the brain. In this paper, we proposed an artificial neural network (ANN) having as output the category of the newborn (healthy, sick or risky) and as input 14 parameters taken from inter-burst intervals of EEG signal. These parameters are detected using a Java application called EEGDiag dedicated to the analysis of EEG. We used a dataset of 397 EEG records detected at birth of premature newborns and their classification two years after birth: healthy, sick or risky. The aim of our work is to provide an automated predication of their prognosis based on their EEG using an ANN. We obtained satisfying results concerning sick class (performance 85.5%) and risky class (performance 90.3%), and we demonstrated the need of extracting new characteristics concerning healthy ones. |
