Biomedical subjects

B Klöppel

Publications and source records attributed to B Klöppel.

8 recordsLinked to original sources

PubMed1998

Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism and points to a frontally pronounced cerebral disturbance.

The capability of predicting relapse in chronic alcoholism using quantitative EEG was investigated. For this purpose, 78 in-patients with alcoholism underwent EEG recordings (eyes closed) 7 days after the beginning of detoxification. Additionally, other clinical evaluations were carried out. After discharge from hospital, patients were regularly re-evaluated for the duration of 3 months in order to determine whether they relapsed or abstained from alcohol during this time. For classification of the two diagnostic subgroups (relapsers vs. abstainers), multivariate discriminant analysis as well as artificial neural network technology has been applied. Correct classification of patients' EEGs was achieved in 83-85% and thus outperformed classification with clinical variables considerably. Furthermore, artificial neural networks (ANN) improved classification results when compared with discriminant analysis. It was found that, in comparison to abstainers, relapsers had EEGs that were more desynchronized over frontal areas, which was interpreted as a functional disturbance of the prefrontal cortex.

Adult↗

PubMed1998

Analysis of quantitative EEG with artificial neural networks and discriminant analysis--a methodological comparison.

Artificial neural networks (ANN) are widely used to solve problems of differentiating between groups. However, serious comparisons of this method with the traditional procedure for such tasks (discriminant analysis) are rare. Discussing the results of both methods with the example of highly topical data, we try to demonstrate advantages and drawbacks of both methods. For this purpose, quantitative EEGs of 78 alcoholics were investigated in order to determine whether it is possible to predict relapse of these patients at the beginning of treatment. ANN software is available in Kassel (Institute for Computer Sciences and Mathematics).

Alcoholism↗

PubMed1995

Automatic recognition of rapid eye movement (REM) sleep by artificial neural networks.

Artificial neural networks are well known for their good performance in pattern recognition. Their suitability for detecting REM sleep periods on the basis of preprocessed EEG data in humans under clinical conditions was tested and their performance compared with the manual evaluation. A single channel of the EEG signal was analysed in time periods of 20 s and preprocessed into a vector of six real numbers, which served as input to the network. EOG and EMG information was ignored. Backpropagation was used as a learning rule for the network, which consisted of 12 neurons and 39 synapses. Training datasets were put together from the input vectors and the corresponding sleep stages were scored manually. In working mode different networks were compared in terms of the rate of misclassified time periods for data not belonging to the training sets. The indicator function of REM sleep was well approximated by the network output in the course of the night, which was especially true for REM onsets. The average rate of correctly classified time periods was 89%. The errors were analysed and suggestions for improvements developed.

Journal Article↗

PubMed1994

Discrimination between demented patients and normals based on topographic EEG slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers.

The topographic distributions of absolute delta and theta powers were used to classify demented patients and normals by means of z statistics, discriminant analysis and artificial neural networks (NN). The data were taken from two psychopharmacological studies in mildly to moderately demented patients (111 and 96 patients for studies I and II, respectively) and from 56 normal healthy controls. All patients were diagnosed according to DSM-III criteria and were free of medication for at least 2 weeks. The NN used was a strictly layered feed-forward network with complete connections. The z-transformed absolute power values in the combined delta and theta frequency range at 17 electrodes, recorded in a 3 min vigilance-controlled EEG with eyes closed, were used as input. After having trained the NN successfully by backpropagating of errors, the generalization test with independent data results in a classification performance of 90% determined by "relative operating characteristic" analysis. The NN out-performed z statistics and discriminant analysis. This high percentage of correct classifications may justify the development of further application of NNs based on topographic EEG data.

Aged↗

PubMed1994

Neural networks as a new method for EEG analysis. A basic introduction.

This paper discusses the general usability of artificial neural networks for the analysis of EEG data. The advantages and drawbacks of this new technology are compared, especially from the perspective of medical computer science. Furthermore this text clarifies the fundamental principles of neural networks, the relations to the biological model and how they compare to classical evaluation methods.

Electroencephalography↗

PubMed1994

Application of neural networks for EEG analysis. Considerations and first results.

This paper presents the results of the practical use of artificial neural networks in the field of EEG analysis. It describes the general methodology of application as well as a case study of a discrimination of depressive and psychotic patients using 16-channel long-term EEG data prepared by classical pre-processing (spectral decomposition). This study shows advantages and current limits concerning different levels of generalisation capabilities using a representative application example.

Electroencephalography↗

PubMed1994

Classification by neural networks of evoked potentials. A first case study.

This paper describes the application of artificial neural networks for the analysis of data of the evoked potential type. A discussion of different preprocessing schemes stresses the importance of a suitable method which supports the artificial neural networks in their classification task. This preprocessing differs completely from well-known data reduction methods used for non-neural classifiers. The examples shown here show the extraordinarily good tolerance against noise, yielding very good classification results for only a small number of observations.

Alcoholism↗

PubMed1992

In vitro production of interleukin-1 from blood mononuclear cells of patients on chronic hemodialysis therapy.

A monocyte defect is thought to be involved in the impaired immune response in patients on regular hemodialysis therapy. As an indicator of cell function, we studied in vitro IL-1 beta production of mononuclear cells from hemodialysis patients in comparison to normal controls. Mononuclear cells were stimulated with endotoxin or Staphylococcus epidermidis in parallel with control incubations in tissue culture medium alone. Spontaneous as well as stimulated total IL-1 beta production (cell-associated plus extracellular) did not differ significantly in cells obtained from patients compared to those from normal controls. However, the relative amounts of IL-1 beta released into the cell supernatants were significantly reduced in mononuclear cells from hemodialysis patients when stimulated with endotoxin but not with Staphylococcus epidermidis. These data indicate a stimulus-dependent defect in the mechanism of IL-1 beta release. As IL-1 is necessary for T-cell activation this alteration in mononuclear cell function may play a role in the impaired cellular immunity observed in patients on chronic hemodialysis therapy.

Culture Media↗