Prediction of Cerebral Palsy in Newborns With Hypoxic-Ischemic Encephalopathy Using Multivariate EEG Analysis and Machine Learning

Prediction of Cerebral Palsy in Newborns With Hypoxic-Ischemic Encephalopathy Using Multivariate EEG Analysis and Machine Learning

This study was carried out to investigate whether the quantitative analysis of electroencephalogram (EEG) signals of infants with hypoxic-ischemic encephalopathy (HIE) can be used for early prediction of cerebral palsy (CP). We computed sample entropy (SampEn), permutation entropy (PEn), and spectra...

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Bibliographic Details
Main Authors: Dalal Bakheet, Noura Alotaibi, Daniel Konn, Brigitte Vollmer, Koushik Maharatna
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Brain connectivity
cerebral palsy (CP)
electroencephalogram (EEG)
empirical mode decomposition (EMD)
hypoxic-ischemic encephalopathy (HIE)
graph theory
Online Access:https://ieeexplore.ieee.org/document/9559923/

Internet

https://ieeexplore.ieee.org/document/9559923/

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