Prof. Herbert Witte, Germany

Institute of Medical Statistics
Computer Sciences and Documentation
Friedrich Schiller University Jena
Jena, Germany
E-mail: Herbert.Witte@mti.uni-jena.de

Herbert Witte was born in 1952. He studied cybernetics at the Technical University of Magdeburg from 1970 to 1974 and received the diploma degree. He then engaged in neurophysiological research, receiving a PhD in neurobiology from the Friedrich Schiller University Jena. In 1986/87 he received the Engineer of Medicine degree in biomedical engineering and the Doctor of Science (habilitation) degree for his work in neonatal monitoring. Since 1992, he has been employed at the Friedrich Schiller University Jena as Professor of Medical Informatics and, in addition, serving as Director of the Institute of Medical Statistics, Computer Sciences and Documentation. He was a member of the Executive Committee of the German Society of Biomedical Engineering 1997-2003, and from 1994-1999 he represented the German Society of Medical Informatics, Medical Statistics and Documentation (GMDS) in the Council of the European Federation for Medical Informatics (EFMI). He also represented the GMDS in the General Assembly of the International Medical Informatics Association (IMIA) from 1999 to 2004.

Prof. Witte was chairman of the IEEE Joint Chapter of the BME (German Section) from 1995 to 2001 and has been Associate Editor of the IEEE Transaction on Biomedical Engineering since 2000. In 2005 he received an honorary doctor degree from the Georgian Technical University (GTU). At the present time he is the Vice-President of the Friedrich Schiller University.

He has published more than 250 articles in journals, books and proceedings. His research fields are neurophysiology (EEG, MEG, fMRI, MRI), intensive care monitoring, signal processing, pattern recognition and neural networks.

Consistent improvement in the measurement techniques of biomedical signals in the last ten years has led to the development of new analysis methods. For example, enhancement of the spatial resolution of EEG records (by an increase in number of electrodes up to 256) has stimulated the development of multivariate (multidimensional) analysis methods. Additionally, the high time resolution and transient properties of the EEG has necessitated development of time-variant (tv), multivariate approaches. These methods developed in EEG analysis can also be used in MEG and fast-MRI studies. Current research interests also focuse on the quantification of transient interrelations between physiological systems as well as between subsystems, i.e. there is no alternative to tv multivariate methods. The paper begins with methodological concepts based on time-variant, multivariate autoregressive models (tv MAR)[2, 6]. By means of time-variant coherence(tvC)[6], partial coherence(tvPC)[2], Granger Causality (tvGC)[3], and partial directed coherence (tvPDC), the directed information transfer between systems can be quantified. The use of these methods in EEG-MEG-analysis, in the analysis of the cardiovascular-respiratory system, and in fast-MRI studies is shown. The tvC has been used to characterize the temporal interrelations between brain structures during a test paradigm for associative learning[5]. In an experimental study, the transient interrelations within the cardiovascular-respiratory system after onset and during hypoxia were quantified by tvPC[2]. Our results in burst-suppression analysis in sedated patients with severe brain injury[7, 9, 10] have been validated by animal experiments[8] using tvGC and tvPDC as appropriate analysis tools. These investigations are all based on measured data. A MEG-study (600Hz SEF components) will be presented in which the influence of a hidden source (without a corresponding measured signal) has been identified. This study is based on a parameter identification of coupled differential equation systems[4]. Finally, pre-liminary results of a fast-fMRI study using tvGC will be presented. The results show that tv MAR models must be replaced by non-linear AR models, e.g. multivariate SETAR or NARX models[1].

1. Arnold, M. and Gunther, R., Adaptive parameter estimation in multivariate self-exciting threshold autoregressive models, Communication in Statistics: Simulation and Computation, 30 (2001) 257-275.
2. Arnold, M., Miltner, W.H., Witte, H., Bauer, R. and Braun, C., Adaptive AR modeling of nonstationary time series by means of Kalman filtering, IEEE Trans Biomed Eng, 45 (1998) 553-62.
3. Hesse, W., Moller, E., Arnold, M. and Schack, B., The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies, J Neurosci Methods, 124 (2003) 27-44.
4. Leistritz, L., Suesse, T., Haueisen, J., Hilgenfeld, B. and Witte, H., Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity., J. Physiol.(Paris) (accepted) (2005).
5. Miltner, W.H., Braun, C., Arnold, M., Witte, H. and Taub, E., Coherence of gamma-band EEG activity as a basis for associative learning, Nature, 397 (1999) 434-6.
6. Moller, E., Schack, B., Arnold, M. and Witte, H., Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models, J Neurosci Methods, 105 (2001) 143-58.
7. Schack, B., Witte, H., Helbig, M., Schelenz, C. and Specht, M., Time-variant non-linear phase-coupling analysis of EEG burst patterns in sedated patients during electroencephalic burst suppression period, Clin Neurophysiol, 112 (2001) 1388-99.
8. Schelter, B., Winterhalter, M., Schwab, K., Leistritz, L., Hesse, W., Bauer, R., Witte, H. and Timmer, J., Quantification of directed signal transfer between neural networks by partial directed coherence. A novel approach to infer causal time-depending influences in noisy, multivariate time series., J Neurosc. Methods (in revision) (2005).
9. Witte, H., Schack, B., Helbig, M., Putsche, P., Schelenz, C., Schmidt, K. and Specht, M., Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period, J Physiol Paris, 94 (2000) 427-34.
10. Witte, H., Schelenz, C., Specht, M., Jager, H., Putsche, P., Arnold, M., Leistritz, L. and Reinhart, K., Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period, Neurosci Lett, 260 (1999) 53-6.