BackgroundOne area where the use of information and communication technology (ICT), or eHealth, could be developed is the home health care of premature infants. The aim of this randomized controlled study was to investigate whether the use of video conferencing or a web application improves parents’ satisfaction in taking care of a premature infant at home and decreases the need of home visits. In addition, nurses’ attitudes regarding the use of these tools were examined.MethodThirty-four families were randomized to one of three groups before their premature infant was discharged from the hospital to home health care: a control group receiving standard home health care (13 families); a web group receiving home health care supplemented with the use of a web application (12 families); a video group with home health care supplemented with video conferencing using Skype (9 families). Families and nursing staff answered questionnaires about the usefulness of ICT. In addition, semi-structured interviews were conducted with 16 families.ResultsAll the parents in the web group found the web application easy to use. 83% of the families thought it was good to have access to their child’s data through the application. All the families in the video group found Skype easy to use and were satisfied with the video calls. 88% of the families thought that video calls were better than ordinary phone calls. 33% of the families in the web group and 75% of those in the video group thought the need for home visits was decreased by the web application or Skype. 50% of the families in the web group and 100% of those in the video group thought the web application or the video calls had helped them feel more confident in caring for their child. Most of the nurses were motivated to use ICT but some were reluctant and avoided using the web application and video conferencing.ConclusionThe families were satisfied with both the web application and video conferencing. The families readily embraced the use of ICT, whereas motivating some of the nurses to accept and use ICT was a major challenge.

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.

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.

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%.