Remote sessions and frequency analysis for improved insight into cerebral function during pediatric and neonatal intensive care

An Exploration of Pediatricians' Professional Identities: A Q-Methodology Study

Professional identities may influence a wide range of attitudes, ethical standards, professional commitments and patient safety. This study aimed to explore the important elements that comprise pediatricians' professional identities. A Q-methodology was used to identify the similarities and differences in professional identity. Forty pediatricians were recruited from two tertiary referral hospitals in Taiwan. A list of statements was developed by five attending physicians and three residents. R software was used to analyze the Q-sorts to load the viewpoints and formulate the viewpoint arrays. Additional qualitative data-one-to-one personal interviews-were analyzed. Twenty-eight of forty pediatricians, 11 males and 17 females, with an average age of 39.9 (27-62) years, were associated with four viewpoints. We labeled the four viewpoints identified for professional identity as (1) professional recognition, (2) patient communication, (3) empathy and (4) insight. The professional recognition viewpoint comprised of youngest participants-28-36 years-with the majority as residents (77.8%), while the empathy viewpoint comprised the oldest participants-38-62 years-with all as attending physicians. All participants in the empathy and insight viewpoints were married. This study found professional identity to be a multifaceted concept for pediatricians, especially in the areas of professional recognition, patient communication, empathy and insight into patient care.

Automatic classification of background EEG activity in healthy and sick neonates

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%.

Classification of burst and suppression in the neonatal electroencephalogram

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.

On evaluation of spectrum estimators for EEG

In the search for how neonatal EEG is affected by asphyxia it is of importance to find reliable estimates of EEG power spectra. Several spectral estimation methods do exist, but since the true spectra are unknown it is hard to tell how well the estimators perform. Therefore a model to generate simulated EEG with known spectrum is proposed and the model is used to evaluate performance of several parametric and Fourier based spectral estimators.

Spectral distance for ARMA models applied to electroencephalogram for early detection of hypoxia

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.