For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular–respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.
Broad, as well as narrow, band Hilbert transform filters (HTFs) were used as preprocessing units in the analysis of electroencephalogram (EEG) and respiratory movements in neonates. For these applications, new algorithms for the adaptation of the resonance frequency of a narrow-band-pass filter to the actual signal properties on the basis of an analytic filter design were developed. For the segmentation of the discontinuous EEG, the location of the resonance frequency was imbedded into the learning algorithm of a neural network (NN). In such automatic EEG pattern recognition, the detection of spike activity was taken into consideration. The spike detection scheme introduced uses broad-band HTFs as basis units. Additionally, the algorithm for the continuous control of the resonance frequency was applied to achieve the adaptation of the processing unit that performed the calculation of the instantaneous respiration rate, in this framework, a new on-line method for adaptive frequency estimation that is less sensitive to low signal-to-noise ratios (SNRs) was obtained. The new approaches introduced were tested in comparison with processing methods that have been established for the analysis of experimental and clinical data.
A method for the construction of optimal structures for feedforward neural networks is introduced. On the basis of a construction of a graph of network structures and an evaluation value which is assigned to each of them, an heuristic search algorithm can be installed on this graph. The application of the A*-algorithm ensures, in theory, both the optimality of the solution and the optimality of the search. For several examples, a comparison between the new strategy and the well-known cascade-correlation procedure is carried out with respect to the performance of the resulting structures.
In this study, an algorithm is introduced for the automatic detection and simultaneous topographic classification of interictal regional spike activity in pediatric surface EEG records. The algorithm is based on the classification of the topographic distribution of instantaneous power by means of a 'group' trained classifier. The results of automatic spike analysis were compared with the decisions of two experienced electroencephalographers. Four routine EEG records exhibiting (multi)regional spikes were examined. The mean selectivity for the automatic spike detector was 84.6% (mean sensitivity 88.1%, mean specificity 89.3%) and for the electroencephalographers 85.3%. All spikes detected by the algorithm were simultaneously classified according to their topographic characteristics. The results of automatic spike classification (lateralization/localization) corresponded to the results of visual analysis.
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