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Aim: To evaluate the prognostic capacity of a new method for automatic quantification of the length of suppression time in the electroencephalogram (EEG) of a group of asphyxiated newborn infants., Methods: Twenty-one full-term newborn infants who had been resuscitated for severe birth asphyxia were studied. Eight channel continuous EEG was recorded for prolonged time periods during the first days of life. Artefact detection or rejection was not applied to the signals. The signals were fed through a pretrained classifier and then segmented into burst and suppression periods. Total suppression length per hour was calculated. All surviving patients were followed with structured neurodevelopmental assessments to at least 18 months of age., Results: The patients who developed neurodevelopmental disability or died had significant suppression periods in their EEG during the first days of life while the patients who had a normal follow-up had no or negligible amount of suppression., Conclusions: This new method for automatic quantification of suppression periods in the raw, neonatal EEG discriminates infants with good from those with poor outcome., (© 2011 The Author(s)/Acta Paediatrica © 2011 Foundation Acta Paediatrica.)

Objective: To study whether indomethacin used in conventional dose for closure of patent ductus arteriosus affects cerebral function measured by electroencephalograms (EEG) evaluated by quantitative measures., Study Design: Seven premature neonates with haemodynamically significant persistent ductus arteriosus were recruited. EEG were recorded before, during and after an intravenous infusion of 0.2 mg/kg indomethacin over 10 min. The EEG was analysed by two methods with different degrees of complexity for the amount of low-activity periods (LAP, "suppressions") as an indicator of affection of cerebral function., Results: Neither of the two methods identified any change in the amount of LAPs in the EEG as compared to before the indomethacin infusion., Conclusion: Indomethacin in conventional dose for closure of patent ductus arteriosus does not affect cerebral function as evaluated by quantitative EEG., (© 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.)

The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

Objective: To evaluate the frequency content of the electroencephalogram (EEG) during recovery after a severe hypoxic insult in newborn piglets., Methods: EEG was continuously monitored in nine newborn piglets exposed to a severe hypoxic period. Power spectra in five frequency bands were calculated using Fourier transformation. Spectral edge frequency 90 (SEF90) was defined as the frequency below which 90% of the power in the EEG was located. The piglets were divided into two groups; Group 1 represented piglets with some EEG recovery and Group 2 represented piglets without any EEG recovery., Results: The recovery of the EEG in Group 1 had the same time course in all frequency bands. SEF90 indicates recovery earlier than the value of total power. But SEF90 also signals activity in the EEGs that were almost completely suppressed. When SEF90 was calculated during periods of periodic EEG activity during the very early phase of recovery, the values fell within the same range as during the control period., Conclusion: Spectral analysis of continuous EEG in newborn piglets exposed to very severe hypoxia showed that no specific frequency band of the EEG preceded the other ones during recovery. The results of the SEF90 measure, demonstrates the need for critical analysis of the raw EEG before any reliable estimation of cerebral function can be made.

Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern. Five features extracted from the EEG were used as inputs. The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods. Based on this, the SVM performs slightly better than the others. Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods.

Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.

Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.

A novel measure of spectral distance is presented, which is inspired by the prediction residual parameter presented by Itakura in 1975, but derived from frequency domain data and extended to include autoregressive moving average (ARMA) models. This new algorithm is applied to electroencephalogram (EEG) data from newborn piglets exposed to hypoxia for the purpose of early detection of hypoxia. The performance is evaluated using parameters relevant for potential clinical use, and is found to outperform the Itakura distance, which has proved to be useful for this application. Additionally, we compare the performance with various algorithms previously used for the detection of hypoxia from EEG. Our results based on EEG from newborn piglets show that some detector statistics divert significantly from a reference period less than 2 min after the start of general hypoxia. Among these successful detectors, the proposed spectral distance is the only spectral-based parameter. It therefore appears that spectral changes due to hypoxia are best described by use of an ARMA- model-based spectral estimate, but the drawback of the presented method is high computational effort.

Cerebral cortical activity in healthy, full-term human neonates (10 boys and 10 girls) was evaluated using spectral estimation of electroencephalogram frequency content with new equipment and analysis technique allowing the assessment of the lowest frequencies (i.e. infraslow waves). The activity was analysed under quiet sleep and active wakefulness taking sex into consideration. During sleep, the mean amount of infraslow activity was 27% larger in boys, whereas during wakefulness the average amount of higher frequencies was 17% larger in girls. Both these differences indicate an earlier maturation of cortical function in girls than in boys.

Eight features inherent in the electroencephalogram (EEG) have been extracted and evaluated with respect to their ability to distinguish bursts from suppression in burst-suppression EEG. The study is based on EEG from six full term infants who had suffered from lack of oxygen during birth. The features were used as input in a neural network, which was trained on reference data segmented by an experienced electroencephalographer. The performance was then evaluated on validation data for each feature separately and in combinations. The results show that there are significant variations in the type of activity found in burst-suppression EEG from different subjects, and that while one or a few features seem to be sufficient for most patients in this group, some cases require specific combinations of features for good detection to be possible.

Objective: To investigate whether very low EEG frequency activity can be recorded from post asphyctic full term neonates using EEG equipment where the high pass filter level was lowered to 0.05 Hz., Methods: The time constant of the amplifier hardware was set to 3.2 s in order to enable recordings that equal to a high pass filter cut off at 0.05 Hz. Burst episodes were selected from the EEGs of 5 post asphyctic full term neonates. The episodes were analysed visually using different montages and subjected to power spectrum analysis. Powers in two bands were estimated; 0-1 and 1-4 Hz, designated very low- and low-frequency activity, respectively (VLFA, LFA)., Results: In all infants, VLFA coinciding with the burst episodes could be detected. The duration of the VLFA was about the same as that of the burst episode i.e. around 4s. The activity was most prominent over the posterior regions. In this small material, a large amount of VLFA neonatally seemed to possibly be related to a more favourable prognosis., Conclusions: VLFA can be recorded from post asphyctic full term neonates using EEG equipment with lowered cut off frequency for the high pass filter., Significance: VLFA normally disregarded due to filtering, is present in the EEG of sick neonates and may carry important clinical information.

Objective: To investigate whether the periodic EEG patterns seen in healthy and sick full term neonates (trace alternant and burst suppression, respectively) have different frequency characteristics., Methods: Burst episodes were selected from the EEGs of 9 healthy and 9 post-asphyctic full-term neonates and subjected to power spectrum analysis. Powers in two bands were estimated; 0-4 and 4-30 Hz, designated low- and high-frequency activity, respectively (LFA, HFA). The spectral edge frequency (SEF) was also assessed., Results: In bursts, the LFA power was lower in periods of burst suppression as compared to those of trace alternant. The parameter that best discriminated between the groups was the relative amount of low- and high-frequency activity. The SEF parameter had a low sensitivity to the group differences. In healthy neonates, the LFA power was higher over the posterior right as compared to the posterior left region., Conclusions: Spectral power of low frequencies differs significantly between the burst episodes of healthy and sick neonates., Significance: These results can be used when monitoring cerebral function in neonates.

A project involving recording and analysing EEG together with cardiovascular signals and temperature has been initiated. The aim of this project is to establish difficulties and possibilities involved with implementing a system for remote sessions and analysing EEG in correlation with other physiological signals. One objective is to find indicators of cerebral function during postasphyxia neonatal intensive care and pediatric cardiopulmonary bypass surgery with hypothermia. Remote sessions for joint interpretation have been carried out between pediatricians and clinical neurophysiologists, and EEG has been analyzed using frequency analyzing tools. One result is the discovery of reversible spectral changes coinciding with blood pressure falls, which may indicate loss of autoregulation function. This finding is one outcome from initial use of a system, developed during the project to facilitate communication about, and analysis of the recorded signals. Thus, already from a limited number of remote sessions and the use of basic signal processing techniques, important results have been achieved and better insight has been gained of how cerebral function is affected by cardiopulmonary bypass surgery. In this paper, we present our experiences from introducing a system for remote consultations, and evaluate the use for such a system in the current applications.