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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
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 spectral entropy (SpEn) measures to reflect the signals’ complexity and the graph-theoretic parameters derived from weighted phase-lag index (WPLI) to measure functional brain connectivity. Both feature sets were calculated in the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain to characterize the tempo-spectral integration of information and thus provide novel insight into the brain dynamics. Statistical analysis results showed a general abnormality in the EEG of individuals with CP at the alpha-band component. Particularly, complexity measures were decreased, and graph-theoretic parameters specified by the diameter feature were increased in infants with CP compared to those with normal neurology. The proposed set of features have also been evaluated using the random under-sampling boosting (RUSBoost) classifier, which was trained and tested on the feature vectors of a cohort of 26 infants - 6 who developed CP by the age of 24 months and 20 with normal neuromotor outcome. A good performance of 84.6% classification accuracy (ACC), 83% sensitivity (SNS), 85% specificity (SPC) and 0.87 area under curve (AUC) was obtained using the entropy features extracted from the alpha-band component. A close result of 80.8% ACC, 67% SNS, 85% SPC and 0.79 AUC was also achieved using the diameter feature calculated from the same frequency range. Therefore, it was concluded that the obtained brain functions’ characteristics successfully discriminate between the two groups of infants. These characteristics could be considered potential biomarkers of cerebral cellular damage and, therefore, could be employed in practical clinical applications for early CP prediction.
2169-3536
137833 - 137846
Bakheet, Dalal, Mohammed
0737830f-fc94-43c6-b009-f2d6c81592a1
Alotaibi, Noura, Meshaan
a2f52ae4-fd47-4b07-bb30-3fdfa9129c33
Konn, Daniel
16577bdb-7225-48b1-96c6-1700276498da
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Bakheet, Dalal, Mohammed
0737830f-fc94-43c6-b009-f2d6c81592a1
Alotaibi, Noura, Meshaan
a2f52ae4-fd47-4b07-bb30-3fdfa9129c33
Konn, Daniel
16577bdb-7225-48b1-96c6-1700276498da
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Bakheet, Dalal, Mohammed, Alotaibi, Noura, Meshaan, Konn, Daniel, Vollmer, Brigitte and Maharatna, Koushik (2021) Prediction of Cerebral Palsy in newborns with hypoxic-ischemic encephalopathy using multivariate EEG analysis and machine learning. IEEE Access, 137833 - 137846.

Record type: Article

Abstract

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 spectral entropy (SpEn) measures to reflect the signals’ complexity and the graph-theoretic parameters derived from weighted phase-lag index (WPLI) to measure functional brain connectivity. Both feature sets were calculated in the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain to characterize the tempo-spectral integration of information and thus provide novel insight into the brain dynamics. Statistical analysis results showed a general abnormality in the EEG of individuals with CP at the alpha-band component. Particularly, complexity measures were decreased, and graph-theoretic parameters specified by the diameter feature were increased in infants with CP compared to those with normal neurology. The proposed set of features have also been evaluated using the random under-sampling boosting (RUSBoost) classifier, which was trained and tested on the feature vectors of a cohort of 26 infants - 6 who developed CP by the age of 24 months and 20 with normal neuromotor outcome. A good performance of 84.6% classification accuracy (ACC), 83% sensitivity (SNS), 85% specificity (SPC) and 0.87 area under curve (AUC) was obtained using the entropy features extracted from the alpha-band component. A close result of 80.8% ACC, 67% SNS, 85% SPC and 0.79 AUC was also achieved using the diameter feature calculated from the same frequency range. Therefore, it was concluded that the obtained brain functions’ characteristics successfully discriminate between the two groups of infants. These characteristics could be considered potential biomarkers of cerebral cellular damage and, therefore, could be employed in practical clinical applications for early CP prediction.

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Prediction_of_Cerebral_Palsy_in_Newborns_With_Hypoxic-Ischemic_Encephalopathy_Using_Multivariate_EEG_Analysis_and_Machine_Learning - Version of Record
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Accepted/In Press date: 20 September 2021
Published date: 5 October 2021

Identifiers

Local EPrints ID: 451931
URI: http://eprints.soton.ac.uk/id/eprint/451931
ISSN: 2169-3536
PURE UUID: 50e1fadf-1efe-4025-a03e-f7539391163c
ORCID for Brigitte Vollmer: ORCID iD orcid.org/0000-0003-4088-5336

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Date deposited: 04 Nov 2021 17:31
Last modified: 17 Mar 2024 03:21

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Contributors

Author: Dalal, Mohammed Bakheet
Author: Noura, Meshaan Alotaibi
Author: Daniel Konn
Author: Koushik Maharatna

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