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Biomedical subjects

Herbert Witte

Publications and source records attributed to Herbert Witte.

13 recordsLinked to original sources
PubMed2006

Alpha entrainment in human electroencephalogram and magnetoencephalogram recordings.

Visual stimulation by repetitive flashes of light can lead to an entrainment of the alpha rhythm in electroencephalogram recordings (also called photic driving). We report a comparison of simultaneously recorded electric and magnetic data in a photic driving experiment, adapted to the individual alpha rhythm of 10 healthy volunteers. We show that there is a stronger frequency entrainment in magnetoencephalogram than in electroencephalogram recordings in all volunteers, which indicates a possible tangential brain activity underlying the dominant entrainment effect. The entrainment in the magnetoencephalogram lasts over significantly more frequencies and is most effective in the region around the individual alpha and a half alpha. For different channels, we found different degrees of entrainment showing topological and time-varying properties.

Adult↗
PubMed2006

Analysis of time-variant quadratic phase couplings in the tracé alternant EEG by recursive estimation of 3rd-order time-frequency distributions.

The quantification of transient quadratic phase couplings (QPC) by means of time-variant bispectral analysis is a useful approach to explain several interrelations between signal components. A generalized recursive estimation approach for 3rd-order time-frequency distributions (3rd-order TFD) is introduced. Based on 3rd-order TFD, time-variant estimations of biamplitude (BA), bicoherence (BC) and phase bicoherence (PBC) can be derived. Different smoothing windows and local moment functions for an optimization of the estimation properties are investigated and compared. The methods are applied to signal simulations and EEG signals, and it can be shown that the new time-variant bispectral analysis results in a reliable quantification of QPC in the tracé alternant EEG of healthy neonates.

Algorithms↗
PubMed2006

Application of generalized dynamic neural networks to biomedical data.

This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology.

Algorithms↗
PubMed2006

Development of interaction measures based on adaptive non-linear time series analysis of biomedical signals.

An important feature of interaction between two signal components is the direction of the interaction. Recently, different methods have been developed and applied for detecting the direction of interactions. Besides frequency-dependent methods, Granger causality is a well-known frequency-independent approach. One popular linear approach is based on autoregressive modeling of the underlying process and evaluates prediction errors under different past assumptions. In the present study, this linear concept is extended to self-exciting threshold autoregressive models, which cover a wider class of processes. An approach for the definition of a state-dependent Granger causality is given and applied to simulated data.

Algorithms↗
PubMed2006

Detection of directed information flow in biosignals.

Several analysis techniques have been developed for time series to detect interactions in multidimensional dynamic systems. When analyzing biosignals generated by unknown dynamic systems, awareness of the different concepts upon which these analysis techniques are based, as well as the particular aspects the methods focus on, is a basic requirement for drawing reliable conclusions. For this purpose, we compare four different techniques for linear time series analysis. In general, these techniques detect the presence of interactions, as well as the directions of information flow, in a multidimensional system. We review the different conceptual properties of partial coherence, a Granger causality index, directed transfer function, and partial directed coherence. The performance of these tools is demonstrated by application to linear dynamic systems.

Algorithms↗
PubMed2004

On the rhythmicity of quadratic phase coupling in the tracé alternant EEG in healthy neonates.

The time-variant quadratic phase coupling (QPC) in trace alternant (TA) EEG patterns in healthy full-term neonates (quiet sleep) was investigated by means of time-variant bispectral analysis. The frequency plain 1-1.5 Hz <=> 3.5-4.5 Hz was used as the region-of-interest. QPC rhythms with a frequency of approximately 0.1 Hz were found in all neonates (n = 6). It can be demonstrated that the QPC rhythm of the TA is generated by a pattern-spanning time-variant phase-locking process characterising early functional interactions in the immature brain.

Brain↗
PubMed2004

Classification of equinus in ambulatory children with cerebral palsy-discrimination between dynamic tightness and fixed contracture.

In this study a generalised dynamic neural network (GDNN) was designed to process gait analysis parameters to evaluate equinus deformity in ambulatory children with cerebral palsy. The aim was to differentiate dynamic calf muscle tightness from fixed muscle contracture. Patients underwent clinical examination and had instrumented gait analysis before evaluating their equinus under anaesthesia and muscle relaxation at the time of surgery to improve gait. The performance of the clinical examination, the subjective interpretation of gait analysis results, and the application of the neural network to assess ankle function were compared to the examination under anaesthesia. Evaluation of equinus by a Neural Network showed high sensitivity and specificity values with a likelihood ratio of +14.63. The results indicate that dynamic calf muscle tightness can be differentiated from fixed calf muscle contracture with considerable precision that might facilitate clinical decision-making.

Algorithms↗
PubMed2004

Improving generalization capabilities of dynamic neural networks.

This work addresses the problem of improving the generalization capabilities of continuous recurrent neural networks. The learning task is transformed into an optimal control framework in which the weights and the initial network state are treated as unknown controls. A new learning algorithm based on a variational formulation of Pontrayagin's maximum principle is proposed. Under reasonable assumptions, its convergence is discussed. Numerical examples are given that demonstrate an essential improvement of generalization capabilities after the learning process of a dynamic network.

Algorithms↗
PubMed2003

Fitting of one ARMA model to multiple trials increases the time resolution of instantaneous coherence.

This study presents a least mean squares (LMS) algorithm for the ensemble modeling of a multivariate ARMA process. Generally, an LMS algorithm makes possible the tracking of parameters for nonstationary time series. Our estimation incorporates multiple process observations that improve the accuracy of the parameter estimation. As a consequence, the estimation sequences come close to the true model parameters with a fast adaptation speed. This advantage also holds true of spectral quantities (e.g., the momentary coherence), which are derived from the model parameters. Thus the extension of the ARMA fitting from one to multiple trajectories allows the investigation of nonstationary biological signals with an increased time resolution. The applicability of the algorithm is demonstrated for event-related EEG coherence analysis of the Sternberg task. The changing interaction between posterior association cortex and anterior brain area was shown for verbal and nonverbal stimuli by means of the time-variant theta coherence.

Electroencephalography↗
PubMed2002

How to avoid spurious cluster validation? A methodological investigation on simulated and fMRI data.

This paper presents an evaluation of a common approach that has been considered as a promising option for exploratory fMRI data analyses. The approach includes two stages: creating from the data a sequence of partitions with increasing number of subsets (clustering) and selecting the one partition in this sequence that exhibits the clearest indications of an existing structure (cluster validation). In order to achieve that the selected partition is actually the best characterization of the data structure, previous studies were directed to find the most appropriate validity function(s). In our analysis protocol, we first optimize the sequence of partitions according to the given objective function. Our study showed that an insufficient optimization of the partition, for one or more numbers of clusters, can easily yield a spurious validation result which, in turn, may lead the analyst to a misleading interpretation of the fMRI experiment. However, a sufficient optimization, for each included number of clusters, provided the basis for a reliable, adequate characterization of the data Furthermore, it enabled an adequate evaluation of the validity functions. These findings were obtained independently for three clustering algorithms (representing the hard and fuzzy clustering variant) and three up-to-date cluster validity functions. The findings were derived from analyses of Gaussian clusters, simulated data sets that mimic typical fMRI response signals, andreal fMRI data. Based on our results we propose a number of options of how to configure improved clustering tools.

Algorithms↗
PubMed2002

Time-variant investigation of quadratic phase couplings caused by amplitude modulation in electroencephalic burst-suppression patterns.

OBJECTIVE: Several studies suggest that parameters derived from bispectral analysis of the EEG can be used to characterize specific cortical activation states represented by non linear interaction between brain electrical oscillations. For example, it was shown that so-called bispectral indices can be used to assess the depth of sedation. The methods applied so far are based on interval-related procedures of bispectral analysis. Consequently, the use of the resulting parameters for on-line monitoring or investigations of signal properties during transition periods, e.g., when entering burst-suppression periods, is restricted. The objective of this paper is to provide the methodological basis for a time-continuous (on-line) investigation of quadratic phase coupling induced by amplitude modulation. METHODS: To accomplish this aim an algorithm is presented which enables the analysis of the temporal development in the degree of amplitude modulation (DAM), e.g., during the transition to burst-suppression periods in patients with severe neurological diseases. RESULTS: It was found that these periods are associated with increasing DAM compared with the baseline conditions.

Algorithms↗
PubMed1997

Common Optimization of Adaptive Preprocessing Units and a Neural Network during the Learning Period. Application in EEG Pattern Recognition.

In this study, a proposition of simultaneous training of the neural network (multilayer perceptron) and adaptive preprocessing unit is presented. This cooperation enables the network to affect the preprocessing and as a consequence to vary the locations of pattern vectors in a feature space. Thus, during the learning process the network tries to find a good separation of classes of patterns, which results in convergence of the whole learning process. The strategy was developed in order to make efficient EEG monitoring in neonates possible. A comparison of the method presented herein with the known learning strategies for neural networks shows the need for using it as an alternative learning process. The convergence of the whole system is also discussed. Copyright 1997 Elsevier Science Ltd.

Journal Article↗