Automatic Detector of Abnormal EEG for Preterm Infants

TitreAutomatic Detector of Abnormal EEG for Preterm Infants
Type de publicationCommunications avec actes
Année de publication2018
Titre de la Conférence/colloqueInternet of Things (IoT) Technologies for HealthCare
Pagination82–87
Dates du congrès, colloque24 octobre 2017
Type de documentProceedings
LangueEnglish
Auteur(s)Jrad, N., Schang D., Chauvet P., Tich S. Nguyen The, Daya B. et Gibaud M.
Directeur(s)Ahmed, M. Uddin, Begum S. et Fasquel J-B.
Université, organismeSpringer International Publishing
Ville, PaysCham
Numéro ISBN978-3-319-76213-5
Résumé

Many of preterm babies suffer from neural disorders caused by birth complications. Hence, early prediction of neural disorders, in preterm infants, is extremely crucial for neuroprotective intervention. In this scope, the goal of this research was to propose an automatic way to study preterm babies Electroencephalograms (EEG). EEG were preprocessed and a time series of standard deviation was computed. These series were thresholded to detect Inter Burst Intervals (IBI). Features were extracted from bursts and IBI and were then classified as Abnormal or Normal using a Multiple Linear Regression. The method was successfully validated on a corpus of 100 infants with no early indication of brain injury. It was also implemented with a user-friendly interface using Java.