Quantitative topographic differentiation of the neonatal EEG

Discrimination of fearful and angry emotional voices in sleeping human neonates: a study of the mismatch brain responses

Appropriate processing of human voices with different threat-related emotions is of evolutionarily adaptive value for the survival of individuals. Nevertheless, it is still not clear whether the sensitivity to threat-related information is present at birth. Using an odd-ball paradigm, the current study investigated the neural correlates underlying automatic processing of emotional voices of fear and anger in sleeping neonates. Event-related potential data showed that the fronto-central scalp distribution of the neonatal brain could discriminate fearful voices from angry voices; the mismatch response (MMR) was larger in response to the deviant stimuli of anger, compared with the standard stimuli of fear. Furthermore, this fear–anger MMR discrimination was observed only when neonates were in active sleep state. Although the neonates' sensitivity to threat-related voices is not likely associated with a conceptual understanding of fearful and angry emotions, this special discrimination in early life may provide a foundation for later emotion and social cognition development.

Validation in principal components analysis applied to EEG data

The well-known multivariate technique Principal Components Analysis (PCA) is usually applied to a sample, and so component scores are subjected to sampling variability. However, few studies address their stability, an important topic when the sample size is small. This work presents three validation procedures applied to PCA, based on confidence regions generated by a variant of a nonparametric bootstrap called the partial bootstrap: (i) the assessment of PC scores variability by the spread and overlapping of “confidence regions” plotted around these scores; (ii) the use of the confidence regions centroids as a validation set; and (iii) the definition of the number of nontrivial axes to be retained for analysis. The methods were applied to EEG data collected during a postural control protocol with twenty-four volunteers. Two axes were retained for analysis, with 91.6% of explained variance. Results showed that the area of the confidence regions provided useful insights on the variability of scores and suggested that some subjects were not distinguishable from others, which was not evident from the principal planes. In addition, potential outliers, initially suggested by an analysis of the first principal plane, could not be confirmed by the confidence regions.

3D statistical parametric mapping of quiet sleep EEG in the first year of life

This paper extends previously developed 3D SPM for Electrophysiological Source Imaging (Bosch et al., 2001) for neonate EEG. It builds on a prior paper by our group that established age dependent means and standard deviations for the scalp EEG Broad Band Spectral Parameters of children in the first year of life. We now present developmental equations for the narrow band log spectral power of EEG sources, obtained from a sample of 93 normal neonates from age 1 to 10 months in quiet sleep. The main finding from these regressions is that EEG power from 0.78 to 7.5 Hz decreases with age and also for 45-50 Hz. By contrast, there is an increase with age in the frequency band of 19-32 Hz localized to parietal, temporal and occipital areas. Deviations from the norm were analyzed for normal neonates and 17 with brain damage. The diagnostic accuracy (measured by the area under the ROC curve) of EEG source SPM is 0.80, 0.69 for average reference scalp EEG SPM, and 0.48 for Laplacian EEG SPM. This superior performance of 3D SPM over scalp qEEG suggests that it might be a promising approach for the evaluation of brain damage in the first year of life.

Maturational changes in automated EEG spectral power analysis in preterm infants

Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10-20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA <32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27(+4) to 36(+3) wk of postmenstrual age (PMA, GA + postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

QEEG norms for the first year of life

BACKGROUND

QEEG allows a more objective evaluation of cerebral electrical activity as well as the production of topographical maps for easier comprehension. Here we have developed qEEG norms for the first year of life using methods previously published for other age ranges, including for example, regression for Gausssianity before Z transformation. These norms constitute a non-invasive and low cost tool for the functional evaluation of the infant's brain.

RESULTS

Developmental equations were obtained from 101 healthy infants recording at spontaneous quiet sleep stage II. Polynomial regression equations, with age as independent variable, were calculated for full Broad Band Spectral Parameters (BBSP) using the Least Squares technique. Interpolated maps of the BBSP values or their Z transformation were constructed for linked-ear reference, average reference and Laplacian montages. All montages produced similar tendency curves and Z maps of absolute and relative power, and mean frequency at all frequency bands. The norms obtained were validated against an independent group of 50 healthy infants and some pathological cases. 91-98% of cases were well classified as normal across all measures and montages. To exemplify, two pathological cases are presented of which their qEEG maps show resemblance to CT and MRI.

CONCLUSIONS

These qEEG norms are highly useful as an aid to visual interpretation and for the study of pathology further evolution as well as for assessment of infants showing brain risk factors. To our knowledge this is the first normative qEEG study for the initial year of life with such large sample and validation-group.

Reference values for amplitude-integrated EEGs in infants from preterm to 3.5 months of age

OBJECTIVES

Amplitude-integrated electroencephalogram (aEEG) is a valuable tool for the continuous evaluation of functional brain maturation in infants. The amplitudes of the upper and lower margins of aEEGs are postulated to change with maturation and correlate with postmenstrual age (PMA). In this study we aimed to establish reliable reference values of aEEG amplitudes, which provide quantitative guidelines for assessing brain maturation as indicated by aEEG results in neonates and young infants.

METHODS

aEEGs from healthy infants (n = 274) with PMAs that ranged from 30 to 55 weeks were divided into 10 groups according to their PMAs. Two 5-minute segments were selected from each aEEG and were used to automatically calculate the upper and lower margins and bandwidths of the aEEG tracings.

RESULTS

Interobserver agreement was achieved with an overall correlation of 0.99. The upper and lower margins of the aEEGs in both active and quiet sleep clearly rose in infants after the neonatal period. The bandwidth defined as the graphic distance decreased almost monotonically throughout the PMA range from 30 to 55 weeks. The lower margin of the aEEG was positively correlated with PMA, with a larger rank correlation coefficient during quiet sleep (r = 0.89) than during active sleep (r = 0.49).

CONCLUSIONS

Reference values of aEEG amplitudes were obtained for infants with a wide range of PMAs and constituted the basis for the quantitative assessment of aEEG changes with maturation in neonates and young infants. The normative amplitudes of aEEG margins, especially of the lower margin in quiet sleep, are recommended as a source of reference data for the identification of potentially abnormal aEEG results.

Multivariate analysis of full-term neonatal polysomnographic data

INTRODUCTION

Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals.

METHODS

We applied methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness. The proportion of these states is a significant indicator of the maturity of the newborn brain in clinical practice. In this study, we used data provided by the Institute for Care of Mother and Child, Prague (12 newborn infants of similar postconceptional age). The data were scored by an experienced physician to four states (wake, quiet sleep, active sleep, movement artifact). For accurate classification, it was necessary to determine the most informative features. We used a method based on power spectral density (PSD) applied to each EEG channel. We also used features derived from electrooculogram (EOG), electromyogram (EMG), ECG, and respiration [pneumogram (PNG)] signals. The most informative feature was the measure of regularity of respiration from the PNG signal. We designed an algorithm for interpreting these characteristics. This algorithm was based on Markov models.

RESULTS

The results of automatic detection of sleep states were compared to the "sleep profiles" determined visually. We evaluated both the success rate and the true positive rate of the classification, and statistically significant agreement of the two scorings was found. Two variants, for learning and for testing, were applied, namely learning from the data of all 12 newborns and tenfold cross-validation, and learning from the data of 11 newborns and testing on the data from the 12th newborn. We utilized information obtained from several biological signals (EEG, ECG, PNG, EMG, EOG) for our final classification. We reached the final success rate of 82.5%. The true positive rate was 81.8% and the false positive rate was 6.1%.

DISCUSSION

The most important step in the whole process is feature extraction and feature selection. In this process, we used visualization as an additional tool that helped us to decide which features to select. Proper selection of features may significantly influence the success rate of the classification. We made a visual comparison of the computed features with the manual scoring provided by the expert. A hidden Markov model was used for classification. The advantage of this model is that it determines the future behavior of the process by its present state. In this way, it preserves information about temporal development.