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OBJECTIVES: The Mini-BESTest consists of items relevant to balance deficiencies among people with Parkinson's disease (PwPD). However, the Mini-BESTest's construct validity has been sparsely evaluated in this population. We therefore aimed to investigate the hypotheses that the Mini-BESTest results would be worse among: (i) PwPD compared to healthy controls; (ii) PwPD with moderate compared to mild motor severity; (iii) PwPD with a history of recurrent compared to non-recurrent falls. Moreover, the relationship between the Mini-BESTest and tests of similar and different constructs was expected to be moderate to strong and poor, respectively. MATERIALS AND METHODS: One hundred and five PwPD with mild-to-moderate motor severity and 47 healthy controls were included. PwPD were divided into subgroups based on motor severity and fall history. Main outcome measures were the Mini-BESTest, the timed up and go (TUG), and the original Unified Parkinson's Disease Rating Scale, part II (Activities of Daily Living). Independent t-tests and Spearman's rho were used for the analyses. RESULTS: The Mini-BESTest results were worse among PwPD compared to controls (P<.001), and among people with moderate motor severity compared to those with mild severity (P<.001). However, no differences were found between recurrent and non-recurrent fallers (P=.096). Spearman's rho showed moderate (ρ=-.470) and poor correlations (ρ=-.211) for convergent (TUG) and divergent validity (UPDRS, part II), respectively. CONCLUSIONS: Overall, the Mini-BESTest appears to adequately measure dynamic balance among PwPD with mild-to-moderate severity, although it was unable to distinguish between recurrent and non-recurrent fallers.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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