Quantitative analysis of high-frequency activity in neonatal EEG

Christopher Lundy; Geraldine B Boylan; Sean Mathieson; Jacopo Proietti; John M O'Toole

doi:10.1016/j.compbiomed.2023.107468

Quantitative analysis of high-frequency activity in neonatal EEG

Comput Biol Med. 2023 Oct:165:107468. doi: 10.1016/j.compbiomed.2023.107468. Epub 2023 Sep 6.

Authors

Christopher Lundy¹, Geraldine B Boylan¹, Sean Mathieson¹, Jacopo Proietti², John M O'Toole³

Affiliations

¹ INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
² INFANT Research Centre, University College Cork, Cork, Ireland; Department of Neurosciences, Biomedicine and Movement, University of Verona, Italy.
³ INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland. Electronic address: jotoole@ucc.ie.

PMID: 37722158
DOI: 10.1016/j.compbiomed.2023.107468

Abstract

Objective: To determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns.

Methods: We compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32-37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5-3 Hz; theta, 3-8 Hz; alpha, 8-15 Hz; beta, 15-30 Hz) and 3 higher-frequency bands (gamma1, 30-48 Hz; gamma2, 52-99 Hz; gamma3, 107-127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort.

Results: We found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12-0.88) and 0.66 (0.36-0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups. High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set.

Conclusions: This is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

Keywords: Clinical neurophysiology; High frequency; Machine learning; Newborn EEG; Preterm; Quantitative EEG.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electrodes
Electroencephalography*
Gestational Age
Humans
Hypoxia-Ischemia, Brain*
Infant, Newborn
Retrospective Studies

Abstract

Publication types

MeSH terms

Grants and funding