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

Introduction

Problem : defective cerebrovascular autoregulation

Δ CBF brain injuries

Premature infants : propensity for development because :

• Δ MABP frequent

• Δ MABP Δ CBF in some infants

1st mean to detect defective autoregulation :

Δ MABP Δ CBF

Acronyms :

• CBF : Cerebral Blood Flow

• MABP : Mean Arterial blood Pressure

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Introduction

But, if Δ SaO2 = 0, then

Δ HbD Δ CBF(hypothesis)

2nd mean to detect defective autoregulation :

Δ MABP Δ HbD with Δ SaO2 = 0

Acronyms :

• HbD : cerebral intravascular oxygenation (=HbO2-HbR)

• SaO2 : arterial oxygen saturation

Aim : allow correction with medication such that Δ CBF=0

Method: The coherence coefficient is a measure of the linear dependence between two signals in the frequency domain.

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Datasets

• 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 ressureHbD : cerebral intravascular oxygenatinonrSO2 : regional oxygen saturationTOI cerebral tissue oxygenationNIRS : near infrared spectroscopySaO2 : arterial oxygen saturation

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Preprocessing

Each artifact point was deleted (Soul et al. 2007)

Step 1: keep signals within normal ranges

Preprocessing

Step 2: remove artifacts in MABP

Preprocessing

Step 3: remove artifacts in SaO2

Preprocessing

Step 4: remove artifacts in HbD

Preprocessing

Preprocessing makes the coherence growing (S. Van Huffel, iSOTT 08)

Preprocessing has bad consequence on the frequency content of the signals

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

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Step 1 : divide signal in epochs (C-windows). Divide each C-window in N segments (called H-windows as it is a highpass filtering)

Welsh coherence

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)

Step 3 : average the N modified H-windows

Step 4 : keep frequency band of interest (fCut : cutoff freq.)

Welsh coherence

Step 5 : compute average amplitude of spectrum in the frequency band of interest

REMARK 1 The FFT (fast Fourier transform) supposes the signals are periodical

REMARK 2 A complete period of the signal should be contained in a each H-window

REMARK 3The 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.

Welsh coherence

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

REMARK 4All parameters satisfy

• TH acts as a highpass filer• The ratio TH/TC should be in the range of 0.51 or

smaller

REMARK 5The 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.

Welsh coherence

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. 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

Welsh coherence

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Critical Score Value

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 testThe 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

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Critical Score Value

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 calibrate2 mean COH (on all infants) on mean correlation coefficient (COR)

+ look at the range of COH and COR._____________________________________________________[1, 2] De Smet., Unpublished

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Morren 1 Soul 2 Wong 3 Optimization

fs 6Hz (0.2Hz)

2Hz (O.4Hz)

1Hz >0.1Hz

(von Siebenthal,

Urlesberger)

TC 30min 10min 20min TH(N+1)/2

TCOver 10min 0min 0min E.g. : TC/2

N 217 3 5 close to 8 or calibr.

TH 12min 5min 10min > 2.5min

(von Siebenthal)

THOver 11min55s 2.5min 7.5min TH/2 if Hanning

(Carter)

fCut 0.01Hz 0.04Hz 0.02Hz <0.05Hz

(von Siebenthal,

Urlesberger, Nyq.)

CSV 0.5 0.77

(Taylor)

0.5 0.5 with calibr. or Taylor’s CSV

In the practice

[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.

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

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

1. Introduction

2. Datasets

3. Preprocessing

4. Sampling frequency

5. Welsh coherence

6. Critical score value

7. In the practice

8. Conclusion

Thanks

toPhD grant Fin. ContributorsResearch Council KULeuvenFlemish GovernmentBelgian Federal Science Policy OfficeEUESA WorkgroupProf. Dr. Ir. S. Van HuffelProf Dr. G. NaulaersLic. J. Vanderhaegen