Research Article
Automatic Detector of Abnormal EEG for Preterm Infants
@INPROCEEDINGS{10.1007/978-3-319-76213-5_12, author={Nisrine Jrad and Daniel Schang and Pierre Chauvet and Sylvie Nguyen The Tich and Bassam Daya and Marc Gibaud}, title={Automatic Detector of Abnormal EEG for Preterm Infants}, proceedings={Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings}, proceedings_a={HEALTHYIOT}, year={2018}, month={2}, keywords={Automatic EEG analysis Inter Burst Interval Detection Feature extraction Multiple Linear Regression Preterm infants}, doi={10.1007/978-3-319-76213-5_12} }- Nisrine Jrad
Daniel Schang
Pierre Chauvet
Sylvie Nguyen The Tich
Bassam Daya
Marc Gibaud
Year: 2018
Automatic Detector of Abnormal EEG for Preterm Infants
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-76213-5_12
Abstract
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.