Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis.

Vladimir Matic,Maarten De Vos,Gunnar Naulaers,Perumpillichira Joseph Cherian,Sampsa Vanhatalo,Ninah Koolen,Sabine Van Huffel,Paul Govaert,Amir H Ansari

doi:10.3389/fnhum.2015.00189

Abstract

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10–60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.

Highlights

Development of neonatal care has led to an increasing interest in continuous brain monitoring for individually optimized neurological treatment
The present study was set out to examine the possibility that neonatal EEG exhibits long-range temporal correlations (LRTC), which can be used as a feature to assess brain condition in a clinically relevant context. This entails answering the following questions: First, is it possible to assess reliable detrended fluctuation analysis (DFA) exponents from the neonatal EEG at clinically relevant time scales from seconds to tens of minutes? Second, if DFA is suboptimal, can metrics from an existing multifractal DFA (MF-DFA) paradigm reflect neonatal EEG in a meaningful way? Third, is it possible to use these measures to distinguish background grades of the neonatal EEG? The last question offers a benchmark to the existing analysis paradigms, while it is directly relevant for development of novel features for automated background EEG classifiers
Our study shows that changes in long range temporal dynamics in the early neonatal EEG may be characterized with the DFA paradigm, the conventional DFA is compromised and the recently introduced multifractal DFA can better disclose

Summary

Introduction

Development of neonatal care has led to an increasing interest in continuous brain monitoring for individually optimized neurological treatment. Recent work in basic neuroscience has provided ample evidence that many brain behaviors exhibit scale-free properties where dynamics of a given feature have no distinct spatial or temporal scale (Beggs and Plenz, 2003; Fransson et al, 2013; Iyer et al, 2014; Roberts et al, 2014) This was recently shown to be the case for the EEG activity of full-term neonates that recover from perinatal asphyxia (Iyer et al, 2014; Roberts et al, 2014), one of the most common reasons for continuous EEG monitoring in the NICUs. Scale-free dynamics in a complex system can give rise to selfsimilarity over temporal scales, i.e., long-range temporal correlations (LRTC), which may be assessed from the EEG using detrended fluctuation analysis (DFA; Peng et al, 1994; Hardstone et al, 2012). This entails answering the following questions: First, is it possible to assess reliable DFA exponents from the neonatal EEG at clinically relevant time scales from seconds to tens of minutes? Second, if DFA is suboptimal, can metrics from an existing MF-DFA paradigm reflect neonatal EEG in a meaningful way? Third, is it possible to use these measures to distinguish background grades of the neonatal EEG? The last question offers a benchmark to the existing analysis paradigms, while it is directly relevant for development of novel features for automated background EEG classifiers

Materials and Methods

Results

Discussion