Coordination of the EEG and the heart rate of preterm neonates during quiet sleep

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

A Bradycardia-Based Stress Calculator for the Neonatal Intensive Care Unit: A Multisystem Approach

Early life stress in the neonatal intensive care unit (NICU) can predispose premature infants to adverse health outcomes and neurodevelopment delays. Hands-on-care and procedural pain might induce apneas, hypoxic events, and sleep-wake disturbances, which can ultimately impact maturation, but a data-driven method based on physiological fingerprints to quantify early-life stress does not exist. This study aims to provide an automatic stress detector by investigating the relationship between bradycardias, hypoxic events and perinatal stress in NICU patients. EEG, ECG, and SpO 2 were recorded from 136 patients for at least 3 h in three different monitoring groups. In these subjects, the stress burden was assessed using the Leuven Pain Scale. Different subspace linear discriminant analysis models were designed to detect the presence or the absence of stress based on information in each bradycardic spell. The classification shows an area under the curve in the range [0.80-0.96] and a kappa score in the range [0.41-0.80]. The results suggest that stress seems to increase SpO 2 desaturations and EEG regularity as well as the interaction between the cardiovascular and neurological system. It might be possible that stress load enhances the reaction to respiratory abnormalities, which could ultimately impact the neurological and behavioral development.

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac processη) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to theη,δ,θ,α,σ,βtime series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP forδandθbut not forη The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Quadratic Phase Couplings in the EEG of Premature and Full-term Newborn during Quiet Sleep

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images".

OBJECTIVES

The aim of this study was to compare rhythmicities in the quadratic phase coupling (QPC) in the tracé discontinue EEG patterns (TD) of premature newborns and the tracé alternant EEG patterns (TA) of full-term newborns by means of time-variant bispectral analysis. Both pattern occur during quiet sleep and are characterized by an ongoing sequence of interburst and burst patterns. The courses of time-variant bispectral measures during the EEG burst most likely indicate specific interrelations between cortical and thalamocortical brain structures.

METHODS

The EEG of a group of premature (n = 5) and of full-term (n = 5) newborns was analysed. Time-variant QPC was investigated by means of time-variant parametric bispectral analysis. The frequency plain [0.5 Hz, 1.5 Hz] x [3 Hz, 6 Hz] was used as the region-of-interest (ROI).

RESULTS

QPC rhythms with a frequency of 0.1 Hz (8 - 11 s) were found in all full-term newborns at all electrodes. For the premature newborns the QPC rhythms were less stable and slower (< 0.1 Hz, 11 -  17 s) at all electrodes and showed a higher inter-individual variation than for the full-term newborns. Statistically, the adaptation of a linear mixed model revealed a difference of about 5 s between both groups of newborns.

CONCLUSIONS

The comparison of the results of both groups of newborns indicates a development in the interaction between cortical, thalamocortical and neurovegetative structures in the neonatal brain.

Time-variant analysis of linear and non-linear phase couplings of and between frequency components of EEG burst patterns in full-term newborns

Time-variant (tv) phase-locking and synchronization characteristics of and between low-frequency (≤ 1.5 Hz) and high-frequency EEG oscillations (≥ 3.5 Hz) of the tracé alternant (TA) pattern in full-term newborns have been quantified to explore the origin of quadratic phase coupling (QPC, as non-linear phase coupling measure) between the frequency ranges 1 - 1.5 Hz ⇔ 3.5 - 4.5 Hz, which characterize the specific interactions of oscillations during the TA's burst activity. Using the Gabor transformation two measures of linear phase coupling, the phase-locking index (PLI) and the n:m phase synchronization index (PSI) have been determined. Phase-locking within the frequency ranges 1 -1.5 Hz and 3.5 - 4.5 Hz, and synchronization between both frequency ranges exists. These phase characteristics are significant 2 sec after burst onset and are associated with the maximum-values of the QPC(1 - 1.5 Hz ⇔ 3.5 - 4.5 Hz) which demonstrates that a specific neuronal coordination between the dynamics of phases and of amplitude-frequency dependencies must be underlying.