Download PDF
Methods Inf Med 2009; 48(01): 18-28
DOI: 10.3414/ME9133
Original Articles
Schattauer GmbH

Signal Informatics as an Advanced Integrative Concept in the Framework of Medical Informatics

New Trends Demonstrated by Examples Derived from Neuroscience
H. Witte
1   Institute of Medical Statistics, Computer Sciences and Documentation, Bernstein Group Computational Neuroscience, Friedrich Schiller University, Jena, Germany
,
M. Ungureanu
1   Institute of Medical Statistics, Computer Sciences and Documentation, Bernstein Group Computational Neuroscience, Friedrich Schiller University, Jena, Germany
,
C. Ligges
2   Department of Child and Adolescent Psychiatry, Friedrich Schiller University, Jena, Germany
,
D. Hemmelmann
1   Institute of Medical Statistics, Computer Sciences and Documentation, Bernstein Group Computational Neuroscience, Friedrich Schiller University, Jena, Germany
,
T. Wüstenberg
3   Department of Medical Psychology, Georg-August University, Göttingen, Germany
,
J. Reichenbach
4   Institute of Diagnostic and Interventional Radiology, Friedrich Schiller University, Jena, Germany
,
L. Astolfi
5   Department of Human Physiology and Pharmacology, University Sapienza, Rome, Italy
6   IRCCS Fondazione S. Lucia, Rome, Italy
,
F. Babiloni
5   Department of Human Physiology and Pharmacology, University Sapienza, Rome, Italy
6   IRCCS Fondazione S. Lucia, Rome, Italy
,
L. Leistritz
1   Institute of Medical Statistics, Computer Sciences and Documentation, Bernstein Group Computational Neuroscience, Friedrich Schiller University, Jena, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
17 January 2018 (online)

    Permissions and Reprints

Summary

Objectives: The main objective is to show current topics and future trends in the field of medical signal processing which are derived from current research concepts. Signal processing as an integrative concept within the scope of medical informatics is demonstrated.

Methods: For all examples time-variant multivariate autoregressive models were used. Based on this modeling, the concept of Granger causality in terms of the time-variant Granger causality index and the time-variant partial directed coherence was realized to investigate directed information transfer between different brain regions.

Results: Signal informatics encompasses several diverse domains including: processing steps, methodologies, levels and subject fields, and applications. Five trends can be recognized and in order to illustrate these trends, three analysis strategies derived from current neuroscientific studies are presented. These examples comprise high-dimensional fMRI and EEG data. In the first example, the quantification of time-variant-directed information transfer between activated brain regions on the basis of fast-fMRI data is introduced and discussed. The second example deals with the investigation of differences in word processing between dyslexic and normal reading children. Different dynamic neural networks of the directed information transfer are identified on the basis of event-related potentials. The third example shows time-variant cortical connectivity networks derived from a source model.

Conclusions: These examples strongly emphasize the integrative nature of signal informatics, encompassing processing steps, methodologies, levels and subject fields, and applications.

Keywords

Time-variant signal analysis - model-related analysis - multivariate approach - high-dimensional and multimodal data
 

  • References

  • 1 Hersh WR. Medical informatics: improving health care through information. JAMA 2002; 288 (16) 1955-1958.
    CrossrefPubMedGoogle Scholar
  • 2 Lehmann TM, Aach T, Witte H. Sensor, signal, and image informatics – state of the art and current topics. Methods Inf Med 2006; 45 (01) 57-67.
    PubMedGoogle Scholar
  • 3 Proakis JG, Manolakis DG. Introduction to digital signal processing. New York: Macmillan Publishing Company; 1989
    Google Scholar
  • 4 Firpi H, Smart O, Worrell G, Marsh E, Dlugos D, Litt B. High-frequency oscillations detected in epileptic networks using swarmed neural-network features. Annals of Biomedical Engineering 2007; 35 (09) 1573-1584.
    CrossrefPubMedGoogle Scholar
  • 5 Worrell GA, Gardner AB, Stead SM, Hu S, Goerss S, Cascino GJ. et al. High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. Brain 2008; 131: 928-937.
    CrossrefPubMedGoogle Scholar
  • 6 Marmarelis PZ, Marmarelis VZ. Analysis of Physiological Systems. New York, London: Plenum Press; 1978
    Google Scholar
  • 7 Schelter B, Dahlhaus R, Leistritz L, Hesse W, Schack B, Kurths J. et al. Multivariate time series analysis. In: Dahlhaus R, Kurths J, Maas P, Timmer J. (eds). Mathematical Methods of Time Series Analysis and Digital Image Processing. Springer: Understanding Complex Systems. Berlin, Heidelberg: Springer-Verlag; 2008. pp 1-40.
    Google Scholar
  • 8 Baccala LA, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biological cybernetics 2001; 84 (06) 463-474.
    CrossrefPubMedGoogle Scholar
  • 9 Mukhopadhyay ND, Chatterjee S. Causality and pathway search in microarray time series experiment. Bioinformatics (Oxford, England) 2007; 23 (04) 442-449.
    CrossrefPubMedGoogle Scholar
  • 10 Arnold M, Miltner WH, Witte H, Bauer R, Braun C. Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE transactions on bio-medical engineering 1998; 45 (05) 553-562.
    CrossrefPubMedGoogle Scholar
  • 11 Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics (Oxford, England) 2007; 23 (13) 1623-1630.
    CrossrefPubMedGoogle Scholar
  • 12 Bruns A. Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches?. Journal of Neuroscience Methods 2004; 137 (02) 321-332.
    CrossrefPubMedGoogle Scholar
  • 13 Nie K, Stickney G, Zeng FG. Encoding frequency modulation to improve cochlear implant performance in noise. IEEE transactions on bio-medical engineering 2005; 52 (01) 64-73.
    CrossrefPubMedGoogle Scholar
  • 14 Winterhalder M, Schelter B, Hesse W, Schwab K, Leistritz L, Klan D. et al. Comparison directed of linear signal processing techniques to infer interactions in multivariate neural systems. Signal Processing 2005; 85 (11) 2137-2160.
    CrossrefPubMedGoogle Scholar
  • 15 Bruns A, Eckhorn R. Task-related coupling from high- to low-frequency signals among visual cortical areas in human subdural recordings. Int J Psychophysiol 2004; 51 (02) 97-116.
    CrossrefPubMedGoogle Scholar
  • 16 Eckhorn R, Gail AM, Bruns A, Gabriel A, Al-Shaikhli B, Saam M. Different types of signal coupling in the visual cortex related to neural mechanisms of associative processing and perception. IEEE transactions on neural networks/a publication of the IEEE Neural Networks Council 2004; 15 (05) 1039-1052.
    PubMedGoogle Scholar
  • 17 Helbig M, Schwab K, Leistritz L, Eiselt M, Witte H. Analysis of time-variant quadratic phase couplings in the trace alternant EEG by recursive estimation of 3rd-order time-frequency distributions. Journal of neuroscience methods 2006; 157 (01) 168-177.
    CrossrefPubMedGoogle Scholar
  • 18 Bal I, Klonowski W. SENSATION – New nanosensors and application of nonlinear dynamics for analysis of biosignals measured by these sensors. IFMBE Proceedings 2007; 14: 735-736.
    PubMedGoogle Scholar
  • 19 Schwab K, Groh T, Schwab M, Witte H. Time-variant analysis of nonlinear stability and bispectral measures to quantify the development of fetal sleep states. Conf Proc IEEE Eng Med Biol Soc 2006; 1: 1454-1457.
    PubMedGoogle Scholar
  • 20 Galka A, Yamashita O, Ozaki T, Biscay R, Valdes-Sosa P. A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. NeuroImage 2004; 23 (02) 435-453.
    CrossrefPubMedGoogle Scholar
  • 21 Astolfi L, Cincotti F, Mattia D, De Vico Fallani F, Tocci A, Colosimo A. et al. Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE transactions on biomedical engineering 2008; 55 (03) 902-913.
    CrossrefPubMedGoogle Scholar
  • 22 Haueisen J, Leistritz L, Susse T, Curio G, Witte H. Identifying mutual information transfer in the brain with differential-algebraic modeling: Evidence for fast oscillatory coupling between cortical somatosensory areas 3b and 1. NeuroImage 2007; 37 (01) 130-136.
    CrossrefPubMedGoogle Scholar
  • 23 Riera J, Aubert E, Iwata K, Kawashima R, Wan X, Ozaki T. Fusing EEG and fMRI based on a bottomup model: inferring activation and effective connectivity in neural masses. Philosophical transactions of the Royal Society of London 2005; 360 1457 1025-1041.
    CrossrefPubMedGoogle Scholar
  • 24 Penny WD, Stephan KE, Mechelli A, Friston KJ. Comparing dynamic causal models. NeuroImage. 2004; 22 (03) 1157-1172.
    CrossrefPubMedGoogle Scholar
  • 25 Buxton RB, Wong EC, Frank LR. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med 1998; 39 (06) 855-864.
    CrossrefPubMedGoogle Scholar
  • 26 Grunling C, Ligges M, Huonker R, Klingert M, Mentzel HJ, Rzanny R. et al. Dyslexia: the possible benefit of multimodal integration of fMRIand EEG-data. J Neural Transm 2004; 111 (07) 951-969.
    PubMedGoogle Scholar
  • 27 Leistritz L, Hesse W, Wustenberg T, Fitzek C, Reichenbach JR, Witte H. Time-variant analysis of fast-fMRI and dynamic contrast agent MRI sequences as examples of 4-dimensional image analysis. Methods Inf Med 2006; 45 (06) 643-650.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 28 Möller E, Schack B, Arnold M, Witte H. Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models. J Neurosci Methods 2001; 105 (02) 143-158.
    CrossrefPubMedGoogle Scholar
  • 29 Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith C, Frackowiak RSJ. Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping 1995; 2: 189-210.
    PubMedGoogle Scholar
  • 30 Cunnington R, Windischberger C, Deecke L, Moser E. The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the Bereitschafts-BOLD response. Neuroimage. 2003; 20 (01) 404-412.
    CrossrefPubMedGoogle Scholar
  • 31 Wildgruber D, Erb M, Klose U, Grodd W. Sequential activation of supplementary motor area and primary motor cortex during self-paced finger movement an human evaluated by functional. Neuroscience Letters. 1997; 227 (03) 161-164.
    CrossrefPubMedGoogle Scholar
  • 32 Yousry TA, Schmid UD, Alkadhi H, Schmidt D, Peraud A, Buettner A. et al. Localization of the motor hand area to a knob on the precentral gyrus – A new landmark. Brain [Article] 1997; 120: 141-157.
    CrossrefPubMedGoogle Scholar
  • 33 Erdler M, Beisteiner R, Mayer D, Kaindl T, Edward V, Windischberger C. et al. Supplementary motor area activation preceding voluntary movement is detectable with a whole-scalp magnetoencephalography system. Neuroimage 2000; 11 6 Pt 1 697-707.
    CrossrefPubMedGoogle Scholar
  • 34 Astolfi L, Cincotti F, Mattia D, Babiloni C, Carducci F, Basilisco A. et al. Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data. Clin Neurophysiol. 2005; 116 (04) 920-932.
    CrossrefPubMedGoogle Scholar
  • 35 Babiloni C, Babiloni F, Carducci F, Cappa SF, Cincotti F, Del Percio C. et al. Human cortical responses during one-bit short-term memory. A high-resolution EEG study on delayed choice reaction time tasks. Clin Neurophysiol 2004; 115 (01) 161-170.
    CrossrefPubMedGoogle Scholar
  • 36 Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA, de Vico Fallani F. et al. Comparison of different cortical connectivity estimators for highresolution EEG recordings. Human brain mapping 2007; 28 (02) 143-157.
    CrossrefPubMedGoogle Scholar
  • 37 Babiloni F, Mattia D, Babiloni C, Astolfi L, Salinari S, Basilisco A. et al. Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle. Magnetic resonance imaging 2004; 22 (10) 1471-1476.
    CrossrefPubMedGoogle Scholar
  • 38 Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008; 7 (11) 1032-1043.
    CrossrefPubMedGoogle Scholar
  • 39 Kim SH, Ryoo DW, Bae C. Adaptive noise cancellation using accelerometers for the PPG signal from forehead. Conf Proc IEEE Eng Med Biol Soc 2007; 2007 pp 2564-2567.
    PubMedGoogle Scholar