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Cognitive outcome prediction in infants with neonatal hypoxic-ischemic encephalopathy based on functional connectivity and complexity of the electroencephalography signal

Cognitive outcome prediction in infants with neonatal hypoxic-ischemic encephalopathy based on functional connectivity and complexity of the electroencephalography signal
Cognitive outcome prediction in infants with neonatal hypoxic-ischemic encephalopathy based on functional connectivity and complexity of the electroencephalography signal
Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.
brain connectivity, cognitive scores, electroencephalography (EEG), entropy analysis, graph theory, hypoxic-ischemic encephalopathy (HIE), noise-assisted multivariate empirical mode decomposition (NA-MEMD)
1662-5161
Alotaibi, Noura, Meshaan
a2f52ae4-fd47-4b07-bb30-3fdfa9129c33
Bakheet, Dalal, Mohammed
0737830f-fc94-43c6-b009-f2d6c81592a1
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Konn, Daniel
32db11f2-48e7-4c80-8012-324ea9babcae
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Alotaibi, Noura, Meshaan
a2f52ae4-fd47-4b07-bb30-3fdfa9129c33
Bakheet, Dalal, Mohammed
0737830f-fc94-43c6-b009-f2d6c81592a1
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Konn, Daniel
32db11f2-48e7-4c80-8012-324ea9babcae
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Alotaibi, Noura, Meshaan, Bakheet, Dalal, Mohammed, Vollmer, Brigitte, Konn, Daniel and Maharatna, Koushik (2022) Cognitive outcome prediction in infants with neonatal hypoxic-ischemic encephalopathy based on functional connectivity and complexity of the electroencephalography signal. Frontiers in Human Neuroscience, 15, [795006]. (doi:10.3389/fnhum.2021.795006).

Record type: Article

Abstract

Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.

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Accepted/In Press date: 10 September 2021
Published date: 27 January 2022
Additional Information: Funding Information: This work is partly funded by the scholarship program of the University of Jeddah, Jeddah, Saudi Arabia. Noura Alotaibi and Dalal Bakheet are awarded this scholarship for a Ph.D. study at the University of Southampton, Southampton, United Kingdom.
Keywords: brain connectivity, cognitive scores, electroencephalography (EEG), entropy analysis, graph theory, hypoxic-ischemic encephalopathy (HIE), noise-assisted multivariate empirical mode decomposition (NA-MEMD)

Identifiers

Local EPrints ID: 455538
URI: http://eprints.soton.ac.uk/id/eprint/455538
ISSN: 1662-5161
PURE UUID: 741a6e4a-aac9-4790-8168-919b93d420d7
ORCID for Brigitte Vollmer: ORCID iD orcid.org/0000-0003-4088-5336

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Date deposited: 24 Mar 2022 17:41
Last modified: 28 Apr 2022 02:03

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Contributors

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

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