Newborn EEG Research Articles

EEG in the healthy term newborn within 12 hours of birth

To characterise and quantify the EEG during sleep in healthy newborns in the early newborn period. Continuous multi-channel video-EEG data was recorded for up to 2 hours in normal newborns within 12 hours of birth. The total amount of active (AS) and quiet sleep (QS) was calculated in the first hour of recording. The EEG signal was quantitatively analysed for symmetry and synchrony. Spectral edge frequency (SEF), spectral entropy (H) and relative delta power (delta(R)) were calculated for a ten-minute segment of AS and QS in each recording. Paired t-test and Wilcoxon rank sum test were used for data analysis. Thirty normal newborn babies were studied, 10 within 6 hours of birth and 20 between 6 and 12 hours. All babies showed continuous symmetrical and synchronous EEG activity and well-developed sleep-wake cycling (SWC) with the median percentage of AS--48.5% and QS--36.6%. Quantitative EEG analysis of sleep epochs showed that SEF and H were significantly higher (p<0.0001) and delta(R) was significantly lower (p<0.0001) in AS than in QS. The normal newborn EEG shows symmetrical and synchronous continuous activity and well-developed SWC as early as within the first 6 hours of birth. Quantitative analysis of the EEG in the early postnatal period reveals differences in SEF, H and delta(R) for AS and QS periods. These findings may have implications for quantitative analysis of the newborn EEG, including the EEG of babies with hypoxic ischaemic encephalopathy.

A time–frequency approach for noise reduction

This paper proposes a technique for reducing noise from a signal's time series using a time-frequency distribution. The technique is based on the SVD of the matrix associated with the time-frequency representation of the signal. In this approach the time-frequency representation of the signal is initially divided into signal subspace and noise subspace using singular values of the time-frequency matrix as a criterion for space division. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix enhances the information embedded in the time-frequency representation of the signal. The proposed approach utilizes the Savitzky-Golay low-pass filter for noise attenuation from the singular vectors. The results of applying the proposed method on both synthetic signals and newborn EEGs indicate superiority of the proposed technique over the existing one in reducing noise from signals.

A multistage knowledge-based system for EEG seizure detection in newborn infants

Objective Automatic seizure detection has attracted attention as a method to obtain valuable information concerning the duration, timing, and frequency of seizures. Methods currently used to detect EEG seizures in adults show high false detection rates in neonates because they lack information about specific age-dependent features of normal and pathological EEG and artifacts. This paper describes a novel multistage knowledge-based seizure detection system for newborn infants to identify and classify normal and pathological newborn EEGs as well as seizures with a reduced false detection rate. Methods We developed the system in a way to make comprehensive use of spatial and temporal contextual information obtained from multichannel EEGs. The system development consists of six major stages: (i) EEG data collection and bandpass filtering; (ii) automatic artifact detection; (iii) feature extraction from segments of non-seizure and seizure activities; (iv) feature selection via the relevance and redundancy analysis; (v) EEG classification and pattern recognition using a trained multilayer back-propagation neural network; and (v) knowledge-based decision-making to examine each of possible EEG patterns from a multi-channel perspective. The system was developed and tested with the EEG recordings of 10 newborns aged between 39 and 42 weeks. Results The overall sensitivity, selectivity, and average detection rate of the system were 74%, 70.1%, and 79.7%, respectively. The average false detection of 1.55/h was also achieved by the system with a feature reduction up to 80%. Conclusions The expert rule-based decision-making subsystem accompanying the classifier helped to reduce the false detection rate, reject a wide variety of artifacts, and discriminate various patterns of EEG. Significance This paper may serve as a guide for the selection of discriminative features to improve the accuracy of conventional seizure detection systems for routine clinical EEG interpretation and brain activity monitoring in newborns especially those hospitalized in the neonatal intensive care units.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity (RSC), which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed RSC measure is used in the analysis of newborn electroencephalogram (EEG). To do this, firstly, a time-frequency decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and nonstationary nature. The model consists of background and seizure submodels. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models have a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

IF estimation for multicomponent signals using image processing techniques in the time–frequency domain

This paper presents a method for estimating the instantaneous frequency (IF) of multicomponent signals. The technique involves, firstly, the transformation of the one-dimensional signal to the two-dimensional time–frequency (TF) domain using a reduced interference quadratic TF distribution. IF estimation of signal components is then achieved by implementing two image processing steps: local peak detection of the TF representation followed by an image processing technique called component linking. The proposed IF estimator is tested on noisy synthetic monocomponent and multicomponent signals exhibiting linear and nonlinear laws. For low signal-to-noise ratio (SNR) environments, a TF peak filtering preprocessing step is used for signal enhancement. Application of the IF estimation scheme to real signals is illustrated with newborn EEG signals. Finally, to illustrate the potential use of the proposed IF estimation method in classifying signals based on their TF components’ IFs, a classification method using least squares data-fitting is proposed and illustrated on synthetic and real signals.

The relationship between ictal activity and sleep stages in the newborn EEG

Objective To verify the relationship of ictal activity and sleep organization (REM/NREM stages) in the neonatal EEG. Methods A retrospective study was developed with 41 high-risk newborns, with clinical history suggestive of seizures and ictal activity identified by the EEG. In each EEG, sleep organization and number/duration of sleep stages were determined. Sleep stages (REM, NREM) were scored following behavior and EEG activity. When it was impossible to recognize stages the epoch was classified as unrecognizable sleep stages (UNSS). Ictal activity was classified according to morphology and correlated with sleep epochs. Density of ictal activity was calculated as the duration of the discharge divided by the duration of the sleep epoch. Results Unrecognizable sleep stages were observed in 21 EEGs (51.2%). Comparing NREM×REM states there was a significant predominance of ictal activity in REM sleep ( P=0.01). Duration of discharges was longer on REM epochs; however, differences were not significant. Considering the type of discharges there was a predominance of monomorphic rhythmic discharges in all sleep epochs. Conclusions In this sample, epileptogenic activity occurred more frequently in UNSS, where sleep organization was already disrupted. When it was possible to recognize sleep stages, ictal activity was more frequently during REM sleep. Significance This study speculates the relationship between sleep organization and ictal activity and raises the hypothesis that the expression of ictal activity in relation to sleep stages (REM/NREM) may have age-dependent mechanisms in the developing brain.

Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

Prenatal predictors of maternal and newborn EEG

Ninety-two mothers were recruited at a prenatal ultrasound clinic at which time they were given the CES-D for depression and the State-Trait Anxiety Inventory, and their urines were assayed for cortisol, norepinephrine, epinephrine, dopamine, and serotonin. At the neonatal period the mothers and neonates were assessed on frontal EEG asymmetry. Correlation analyses revealed the following: (1) the mothers’ frontal asymmetry was negatively related to prenatal depression (CES-D) symptoms, negatively related to prenatal norepinephrine levels and positively related to prenatal serotonin levels; (2) the frontal asymmetry of the newborn was positively correlated with the mothers’ frontal asymmetry and negatively correlated with the mothers’ prenatal depression (CES-D) symptoms and negatively correlated with the mothers’ prenatal state anxiety scores. The neonates’ EEG frontal asymmetry was also, like the mother's, negatively related to prenatal maternal norepinephrine and positively related to prenatal maternal serotonin.

Integrative Cortical Functions in the Immature Brain – Investigations in Human Newborns using EEG

The perinatal period in human beings is characterized by rapid developmental changes of the CNS. Results from animal experiments indicate that spontaneous activity may be of essential importance for this development. Insights into the functional organization can be provided by EEG, but up to now only limited information is available. We hypothesize that highly organized functional patterns are generated in newborn brains in spite of their immaturity. We investigated healthy full-term newborns by multichannel EEG during quiet sleep. Part of the EEG is characterized by the recurrent appearance of high voltage periods (burst) and low voltage periods (interburst). Pattern-related analysis of this EEG was performed by calculating instantaneous spectral power, adaptive inter- and intrahemispheric coherence and the coupling between different frequency bands by bicoherence analysis. Burst periods reappeared during discontinuous EEG with a dominant frequency of about 0.1Hz. During bursts mean spectral power reached a maximum within frequency bands >2.8–14.8Hz during the first part of this period. Spectral power within the other frequency bands and during interburst periods was distributed equally. The highest coherence level was observed during burst periods in comparison to interburst periods. Maximal coherence was reached at different moments during bursts – late in the low frequency bands (0.5–1.5Hz; about 3s after the burst started) and earlier in higher frequency bands (>2Hz; about 2s). The interhemispheric coherence was highest in all frequency bands over the frontal region as compared to the central region during bursts. Mainly in frontal regions phase coupling between high and low frequency components could be described with a temporal dynamic of about 0.1Hz. It can be shown that during the early period of neurodevelopment, characteristic changes of electric potential occur, functional coupling between hemispheres exists and that the EEG expresses highly organized topographic and temporal patterns. Thus, using pattern-related investigations with high temporal resolution the newborn EEG provides evidence of early integrative cortical functions.

Time–frequency based newborn EEG seizure detection using low and high frequency signatures

The nonstationary and multicomponent nature of newborn EEG seizures tend to increase the complexity of the seizure detection problem. In dealing with this type of problem, time–frequency based techniques were shown to outperform classical techniques. Neonatal EEG seizures have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. Seizure detection techniques have been proposed that concentrate on either low frequency or high frequency signatures of seizures. They, however, tend to miss seizures that reveal themselves only in one of the frequency areas. To overcome this problem, we propose a detection method that uses time–frequency seizure features extracted from both low and high frequency areas. Results of applying the proposed method on five newborn EEGs are very encouraging.

Developmental changes of auditory-evoked fields in fetuses

Fetal magnetoencephalography (fMEG) could be a viable noninvasive tool to investigate the development of neuronal functioning in the unborn by means of the maturation of cortical auditory-evoked fields (CAEF). The earliest successful recording of a CAEF component was performed at the gestational age of 27 completed weeks with an observed peak latency of 294 ms. Several different components could be differentiated in the CAEF complex. They were classified in accordance with a nomenclature introduced for cortical auditory-evoked potential (AEP) components in preterm neonates. During the third trimester of pregnancy, the detection of any component was successful in 77% of the cases. The detection rates for the different components observed were P1pm 31%; N1pm 21%; P2pm 27%; N2pm 39%; and P3pm 27%. The development of fetal CAEF is described both intraindividually in longitudinal observations and interindividually by grouped comparison throughout the second half of the pregnancy in the largest series of subjects in the literature. The components P2pm and N2pm show a significant negative correlation of their peak latencies with increasing gestational age ( P < 0.05). fMEG allows to observe the development of a parameter of prenatal cortical function that can be interpreted as maturation. The results are in good accordance with those obtained in newborn EEG studies in preterm neonates.

SVD-based newborn EEG seizure detection in the time-frequency domain

This paper utilises the Singular Value Decomposition (SVD) technique applied to the time-frequency representation of Electroencephalogram (EEG) signals for detecting EEG seizures in neonates. Seizure in EEG signal may have signature in different frequency areas. This paper, is concentrated on the low frequency (lower than 10 Hz) signature of the seizures. The proposed technique uses the estimated distribution function of the singular vectors associated with the time-frequency representation of the EEG epoch to characterise the patterns embedded in the signal. The estimated distributed functions related to the seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

Time-varying statistical dimension analysis with application to newborn scalp EEG seizure signals

A new approach to the analysis of nonstationary possibly nonlinear time series is presented. It is based on an adaptive autocovariance eigenspectrum computation known as APEX together with the Rissanen’s Minimum Description Length criterion for the selection of the most relevant eigenvalues. A new concept of time-varying instantaneous statistical dimension is introduced. The motivation for this new approach is the analysis of newborn electroencephalogram for which nonstationarity is an inherent property. The proposed algorithm and new dimension are first assessed on synthetic data. Then, newborn scalp EEG data are analyzed using the proposed scheme. Transitions between different brain states are shown to occur on a baby having electrical and clinical seizures.

A time-frequency approach for newborn seizure detection.

Techniques previously designed for seizure detection in newborns using the electroencephalogram (EEG) have been relatively inefficient due to their assumption of local stationarity of the EEG. To overcome the problem raised by the nonstationarity of the EEG signal, current methods are extended to a time-frequency approach. This allows the analysis and characterization of the different newborn EEG patterns that are intended to be the first step toward an automatic time-frequency seizure detection and classification. An in-depth analysis of both the autocorrelation and spectrum seizure detection techniques identified the detection criteria that can be extended to the time-frequency domain. The selected method uses a high-resolution reduced interference time-frequency distribution referred to as the B-distribution (BD). Here, the authors present the various patterns of observed time-frequency seizure signals and relate them to current knowledge of seizures. In particular, initial results indicate that a quasilinear instantaneous frequency (IF) can be used as a critical feature of the EEG seizure characteristics.

Preprocessing and time-frequency analysis of newborn EEG seizures.

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Seizure detection of newborn EEG using a model-based approach.

Seizures are often the first sign of neurological disease or dysfunction in the newborn. However, their clinical manifestation is often subtle, which tends to hinder their diagnosis at the earliest possible time. This represents an undesirable situation since the failure to quickly and accurately diagnose seizure can lead to longer-term brain injury or even death. In this paper we consider the problem of automatic seizure detection in the neonate based on electroencephalogram (EEG) data. We propose a new approach based on a model for the generation of the EEG, which is derived from the histology and biophysics of a localized portion of the brain. We show that by using this approach, good detection performance of electrographic seizure is possible. The model for seizure is first presented along with an estimator for the model parameters. Then we present a seizure-detection scheme based on the model parameter estimates. This scheme is compared with the quadratic detection filter (QDF), and is shown to give superior performance over the latter. This is due to the ability of the model-based detector to account for the variability (nonstationarity) of the EEG by adjusting its parameters appropriately.

Quantitative analysis of discontinuous EEG in premature and full-term newborns during quiet sleep

To assess the spatio-temporal structure of discontinuous EEG tracing in mature and immature newborns, we analysed mean spectral power in frequency bands between 0.8 and 16.8 Hz in 6 full-term newborns and 7 premature newborns <32 weeks of conceptional age. The most striking results showed a significantly higher mean spectral power for the first half of bursts than for the second half recorded in >2.8–14.8 Hz frequency bands. This pattern was more pronounced in premature than in full-term newborns. No clear differences were observed in comparisons between the first and the second half of the interburst periods. In addition, as far as mid and high frequency band spectra were considered, the mean spectral power of burst was, in both groups, higher in the right as compared to the left occipital regions.

Evaluation of an automatic seizure detection method for the newborn EEG

In another publication, we described a set of methods for automatic detection of EEG seizures in the newborn. We describe here the evaluation of these methods using a completely new set of data, which were not used in developing the method. This testing data set consisted of recordings from 54 patients, lasting an average of 4.4 h. Recordings had 8–16 channels and were obtained, in approximately equal numbers, from 3 institutions in Canada, the USA and Australia. Recording conditions varied from short recordings fully attended by a technologist to overnight recordings largely unattended. The average seizure detection rate was 69% (77%, 53%, 84% in the 3 institutions). False detections occurred at the average rate of 2.3/h (4.1, 1.0, 2.7 in the 3 institutions), with fluctuations that reflected largely the technical quality and level of supervision of the recordings. The results are similar to those obtained in the commonly used method of epilepsy monitoring in adults and allow us to envisage clinical application.

Characteristics of the newborn EEG as potential teratological markers

Characteristics of the newborn EEG as potential teratological markers