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Comparison of coherence measures for assessment of impaired cerebral autoregulation

Comparison of coherence measures for assessment of impaired cerebral autoregulation. D. De Smet*, J. Vanderhaegen**, G. Naulaers** and S. Van Huffel*. KATHOLIEKE UNIVERSITEIT LEUVEN, BELGIUM *DEPARTMENT OF ELECTRICAL ENGINEERING (ESAT-SCD)

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Comparison of coherence measures for assessment of impaired cerebral autoregulation

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  1. Comparison of coherence measures for assessment of impaired cerebral autoregulation D. De Smet*, J. Vanderhaegen**, G. Naulaers** and S. Van Huffel* KATHOLIEKE UNIVERSITEIT LEUVEN, BELGIUM *DEPARTMENT OF ELECTRICAL ENGINEERING (ESAT-SCD) **NEONATAL INTENSIVE CARE UNIT, UNIVERSITY HOSPITALS LEUVEN

  2. Introduction • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • Problem :defective cerebrovascular autoregulation • ΔCBF brain injuries • Premature infants : propensity for development because : • ΔMABP frequent • ΔMABP ΔCBF in some infants • 1st mean to detect defective autoregulation : • Δ MABP Δ CBF • Acronyms : • CBF : Cerebral Blood Flow • MABP : Mean Arterial blood Pressure

  3. Introduction • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • But, if ΔSaO2 = 0, then • ΔHbD ΔCBF (hypothesis) • 2nd mean to detect defective autoregulation : • ΔMABP ΔHbD with ΔSaO2 = 0 • Acronyms : • HbD : cerebral intravascular oxygenation (=HbO2-HbR) • SaO2 : arterial oxygen saturation • Aim : allow correction with medication such that ΔCBF=0 Method: The coherence coefficient is a measure of the linear dependence between two signals in the frequency domain.

  4. Datasets • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • More than 50 premature infants with need for intensive care from the hospitals of Zürich, Utrecht, and Leuven. • MABP, SaO2, and the NIRS-measured HbD/rSO2/TOI measured simultaneously in the first days of life. • Acronyms : • MABP : mean arterial blood ressure • HbD : cerebral intravascular oxygenatinon • rSO2 : regional oxygen saturation • TOI cerebral tissue oxygenation • NIRS : near infrared spectroscopy • SaO2 : arterial oxygen saturation

  5. Preprocessing Each artifact point was deleted (Soul et al. 2007) Step 1: keep signals within normal ranges

  6. Preprocessing Step 2: remove artifacts in MABP

  7. Preprocessing Step 3: remove artifacts in SaO2

  8. Preprocessing Step 4: remove artifacts in HbD

  9. Preprocessing Preprocessing makes the coherence growing (S. Van Huffel, iSOTT 08) Preprocessing has bad consequence on the frequency content of the signals

  10. Sampling frequency • Condition :sampling frequency (fs) > signal fluctuation frequency • Cyclical fluctuations in the case of continuously measured signals : • CBV/HbTot : 2 to 4.7 cycles/min1 and 3 to 6 cycles/min2 (by NIRS) • MABP : One cycle every 1 to 2.5 min1 • fs > 0.1Hz • Acronyms : • CBV : cerebral blood volume • HbTot : total haemoglobin • MABP : mean arterial blood pressure • _____________________________________________________ • [1] von Siebenthal et al., Brain & Development, 1999. • [2] Urlesberger et al., Neuropediatrics, 1998 • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion

  11. Welsh coherence Step 1 :divide signal in epochs (C-windows). Divide each C-window in N segments (called H-windows as it is a highpass filtering) Step 2 : (1) detrend, (2) apply Hanning windowing, and (3) compute the PSD/CSD for each H-window • Acronyms : • Pxy(f) : crosspower spectral density (CSD) of x(t) and y(t) at a given frequency f • Pxx(f), Pyy(f) : power spectral densities (PSD) of x(t), respectively y(t)

  12. Welsh coherence Step 3 : average the N modified H-windows Step 4 :keep frequency band of interest (fCut : cutoff freq.) Step 5 :compute average amplitude of spectrum in the frequency band of interest

  13. Welsh coherence REMARK 1 The FFT (fast Fourier transform) supposes the signals are periodical REMARK 2A complete period of the signal should be contained in a each H-window REMARK 3 The higher the value of N, the lower the variance of the estimates of the spectra (SNR grows) Problem :if N is too large, then the amplitude of the spectra diminishes1 N close to 82,3 _____________________________________________________ [1] De Smet., Unpublished, 2007. [2] Kay, Prentice Hall, 1988 (book). [3] Taylor, Circulation, 1998. • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion

  14. Welsh coherence • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • REMARK 4 • All parameters satisfy • TH acts as a highpass filer • The ratio TH/TC should be in the range of 0.51 or smaller • REMARK 5 • The ratio THOver/TH should be equal to 0.5 if a Hanning window was applied to the H-windows prior to the periodogram average2. • _____________________________________________________ • [1] De Smet., Unpublished, 2007. • [2] Carter, IEEE Trans. on Audio and Electroacoustics, 1973.

  15. Welsh coherence • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • Consequence :TC= TH(N+1)/2 • TH and TH/TC are the sole • parameters we really can • choose • But … REMARK 6 TH have to satisfy : • TH < TC • TH > 10s1,2 • Acronyms : • TC : duration of C-window (calculation window) • TH : duration of H-window (highpass filtering window) • N : number of averages in the Welsh method • _____________________________________________________ • [1] von Siebenthal et al., Brain & Development, 1999. • [2] Urlesberger et al., Neuropediatrics, 1998

  16. Critical Score Value • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • The critical score value (CSV) is the value above which the amplitude of coherence witnesses a significant linear concordance between the input signals. • Possible value for CSV are : • CSV=0.51 • or2 • Acronyms : •  : significance level (e.g. 0.05) : to be chosen • d : 2.83*TC/TH (for Hanning window) : TH to be chosen • F :F hypothesis test • The significance level  greatly influences the CSV, in • the case that the remainder parameters are unchanged!! • _____________________________________________________ • [1] De Boer et al., Med. Biol. Eng. Comput., 1985. • [2] Taylor et al., Circulation, 1998

  17. Critical Score Value • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion Problem :this formula doesn’t take into account THOver (and thus N) that also has an influence on the amplitude of the coherence spectrum1 Solution : keep working with CSV=0.5 (two signals based on 50% shared variance), and calibrate2mean COH (on all infants) on mean correlation coefficient (COR) + look at the range of COH and COR. _____________________________________________________ [1, 2] De Smet., Unpublished

  18. In the practice • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion [1] Morren et al., Proc. of the Intern. Conf. of IEEE, 2001. [2] Soul et al., Pediatric Research, 2007. [3] Wong et al., Pediatrics, to be published.

  19. Conclusion • Introduction • Datasets • Preprocessing • Sampling frequency • Welsh coherence • Critical score value • In the practice • Conclusion • nice preprocessing but bad consequence on the frequency content of the signals • the problem of the varying amplitude of COH is solved by another manner Taylor did it. We showed Taylor’s method does not account for the overlap between H-windows • we proposed optimized parameters to apply the coherence method

  20. Thanks to PhD grant Fin. Contributors Research Council KULeuven Flemish Government Belgian Federal Science Policy Office EU ESA Workgroup Prof. Dr. Ir. S. Van Huffel Prof Dr. G. Naulaers Lic. J. Vanderhaegen

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