Newborn EEG connectivity analysis using time-frequency signal processing techniques

Abstract

Scalp electroencephalography (EEG) monitoring in early life is providing vital assistance to clinical management during diagnosis and treatment of developing brain disorders. It is also a useful tool for predicting long-term neurological outcomes of perinatal injuries. Continuous evolution of cerebral structures during early brain maturation imposes salient changes in the temporal morphology of newborn EEG patterns. Therefore, dealing with neonatal EEG signals is technically very challenging. The current dominant approach to newborn EEG assessment is based on visual inspection of EEG recordings by an expert. This process is highly subjective and also, very timeconsuming. More importantly, it requires a high level of expertise for appropriate interpretation. On the other hand, neonatal intensive care units (NICUs) in most countries do not have ready access to high-level EEG expertise. The development of automatic newborn EEG analysis systems is therefore vital to improve international newborn health.This dissertation is focused on developing scalp EEG connectivity analysis methods for objective monitoring of newborn EEG signals. Three important characteristics of the signals are taken into consideration for the proposed methodologies: time-varying behavior, directional relationships between channels and frequency-specific fluctuations of amplitudes. It leads to a more comprehensive insight into the scalp-level electrical interactions between different cortical areas of the newborn brain in time and/or frequency domains. Two types of newborn brain abnormalities including seizures and intra-ventricular hemorrhage (IVH) and their impact on scalp EEG connectivity are investigated. Also, neonatal electric resting state networks (eRSNs) are characterized using EEG recordings of healthy fullterms, healthy preterms and preemies with IVH as well as a set of newborn functional magnetic resonance imaging (fMRI) datasets.The first contribution of this research is a new time-frequency based approach for estimating the generalized phase synchrony (GePS) among multichannel newborn EEG signals using the linear relationships between their instantaneous frequency laws. Since the underlying signals are usually multicomponent, a decomposition method like multi-channel empirical mode decomposition is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). The results confirm that the GePS value within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during interictal periods.The second contribution is a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded newborn EEG signals in a way that is less affected by volume conduction and amplitude scaling. The time-varying generalized partial directed coherence method is modified, by orthogonalization ...

Artificial Intelligence-assisted Care for Human Newborns with Neurological Impairments

“…Connectivity studies have shown that long range connections within the brain of the newborn develop much quicker than short range cortical connections, thus making unilateral autonomous cortical activity in newborn babies to be dominant, with no substantial interhemispheric relationship noted at this point [32] . In the first few months, expansion of neuronal pathways occur, which give rise to dynamic changes in the EEG waveform in due course [32] .…”

“…Connectivity studies have shown that long range connections within the brain of the newborn develop much quicker than short range cortical connections, thus making unilateral autonomous cortical activity in newborn babies to be dominant, with no substantial interhemispheric relationship noted at this point [32] . In the first few months, expansion of neuronal pathways occur, which give rise to dynamic changes in the EEG waveform in due course [32] . The general dynamic characteristic of a newborn's EEG is intermittent and discontinuous activity alongside a mixture of bursts of spontaneous activity transients (SAT), and general low voltage activity, which is distinct from burst suppression activity that mostly appears in newborns with existing brain damage [32] .…”

“…In the first few months, expansion of neuronal pathways occur, which give rise to dynamic changes in the EEG waveform in due course [32] . The general dynamic characteristic of a newborn's EEG is intermittent and discontinuous activity alongside a mixture of bursts of spontaneous activity transients (SAT), and general low voltage activity, which is distinct from burst suppression activity that mostly appears in newborns with existing brain damage [32] .…”

Tackling the challenges of group network inference from intracranial EEG data

“…The data preparation and the analysis were performed in MATLAB 2020b (Mathworks, Inc.). DTF was computed using MATLAB implementation (Omidvarnia, 2020;Omidvarnia et al, 2011). Vector autoregression coefficients were computed using the ARFIT package (Neumaier and Schneider, 2001).…”