Data Acquisition and Processing

Part of the Bioanalysis book series (BIOANALYSIS, volume 9)


Four sources of data have been picked up following the conditions of the ethics commission: from students in a dormitory in the framework of a thesis, from 50 patients with diseases in the cardiological department in a hospital, some test samples from premature infants in a hospital for children and a few more at the IOSB, a Fraunhofer Institute. Two vibrometers with wavelength \(\lambda = 1550\) nm and \(\lambda = 633\) nm which are designed for industrial application were used to pick up the measured data. In a preprocessing step the sampling frequency of the laser signal has been reduced, the mean has been suppressed, and the low frequency components of breathing and the higher frequency components of the heartbeat and the heart sounds, respectively, have been separated. Four methods for frequency estimation have been investigated: three of them are block oriented and estimate the frequency by the Fourier transform, the autocorrelation function and zero-crossing, and the last one exploits also zero-crossings but continuously. Few tests have been made to determine the frequency by extracting the peaks of the measured vibrometer signals. For the validation of these methods, a synthetic periodic signal has been designed and the influence of corruption by an additive white Gaussian noise signal and a filtered noise process has been investigated. Since the uncovered thorax is accessible only in hospitals the focus has been on other measurement points, e.g., on the neck under various incident angles.


  1. 1.
    Jeliffe, R.W.: Fundamentals of Electrocardiography. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  2. 2.
    Kammeyer, K.-D., Kroschel, K.: Digitale Signalverarbeitung. Springer-Vieweg, Wiesbaden (2018)CrossRefGoogle Scholar
  3. 3.
    Klinke, R., Silbernagl, S. (eds.): Lehrbuch der Physiologie, 3rd. ed. Thieme , Stuttgart (2001)Google Scholar
  4. 4.
    Kroschel, K., Rigoll, G., Schuller, B.: Statistische Informationstechnik. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Kullbach, S.: Information Theory and Statistics. Wiley, Hoboken (1969)Google Scholar
  6. 6.
    Luik, A., Mignanelli, L., Kroschel, K., Schmitt, C., Rembe, C., Scalise, L.: Laser Doppler vibrometry for non-contact identification and classification of AV-blocks. Futur. Cardiol. 12, 269–279 (2016). Scholar
  7. 7.
    Mignanelli, L., Luik, A., Kroschel, K., Scalise, L., Rembe, C.: Auswertung von Vibrometersignalen zur Bestimmung kardiovaskulärer Parameter. tm - Technisches Messen, vol. 83, pp. 462–473 (2016).
  8. 8.
    Morbiducci, U., Scalise, L., De Melis, M., Grigioni, M.: Optical vibrocardiography: a novel tool for the optical monitoring of cardiac activity. Ann. Biomed. Eng. 35(1), 45–58 (2007)CrossRefGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
    Smith, M.A., Chen, T.: Handbook of Image and Video Processing, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  12. 12.
    Tabatabai, H., Oliver, D.E., Rohrbough, J.W., Papadopoulos, C.: Novel applications of laser Doppler vibration measurement to medical imaging. Sens Imaging 14, 13–28 (2005; 2013)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VIDFraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSBKarlsruheGermany

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