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Wavelet cross-correlation to investigate regional variations in cerebral oxygenation in infants supported on extracorporeal membrane oxygenation.
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PMID:  22879034     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Extracorporeal membrane oxygenation can potentially affect cerebral blood flow dynamics and consequently influence cerebral autoregulation. We applied wavelet cross-correlation (WCC) between multichannel cerebral oxyhemoglobin concentration (HbO(2)) and mean arterial pressure (MAP), to assess regional variations in cerebral autoregulation. Six infants on veno-arterial (VA) ECMO were studied during sequential changes in the ECMO flows. WCC between MAP and HbO(2) for each flow period and each channel was calculated within three different frequency (wavelet scale) bands centered around 0.1, 0.16, and 0.3 Hz chosen to represent low frequency oscillations, ventilation, and respiration rates, respectively. The group data showed a relationship between maximum WCC and ECMO flow. During changes in ECMO flow, statistically significant differences in maximum WCC were found between right and left hemispheres. WCC between HbO(2) and MAP provides a useful method to investigate the dynamics of cerebral autoregulation during ECMO. Manipulations of ECMO flows are associated with regional changes in cerebral autoregulation which may potentially have an important bearing on clinical outcome.
Authors:
Maria Papademetriou; Ilias Tachtsidis; Martin J Elliott; Aparna Hoskote; Clare E Elwell
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Advances in experimental medicine and biology     Volume:  765     ISSN:  0065-2598     ISO Abbreviation:  Adv. Exp. Med. Biol.     Publication Date:  2013  
Date Detail:
Created Date:  2012-08-10     Completed Date:  2012-12-11     Revised Date:  2014-06-03    
Medline Journal Info:
Nlm Unique ID:  0121103     Medline TA:  Adv Exp Med Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  203-9     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Arterial Pressure
Brain / blood supply*,  physiopathology
Cerebrovascular Circulation*
Extracorporeal Membrane Oxygenation / methods*
Homeostasis
Humans
Infant, Newborn
Monitoring, Physiologic*
Oxygen / metabolism*
Oxyhemoglobins / metabolism
Respiratory Insufficiency / prevention & control*
Wavelet Analysis*
Grant Support
ID/Acronym/Agency:
G0701458//Medical Research Council
Chemical
Reg. No./Substance:
0/Oxyhemoglobins; S88TT14065/Oxygen
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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Journal Information
Journal ID (nlm-ta): Adv Exp Med Biol
Journal ID (iso-abbrev): Adv. Exp. Med. Biol
Journal ID (publisher-id): Adv. Exp. Med. Biol.
ISSN: 0065-2598
Publisher: Springer
Article Information
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© Springer Science+Business Media New York 2013
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Electronic publication date: Year: 2013
Volume: 765First Page: 203 Last Page: 209
PubMed Id: 22879034
ID: 4038005
DOI: 10.1007/978-1-4614-4989-8_28

Wavelet Cross-Correlation to Investigate Regional Variations in Cerebral Oxygenation in Infants Supported on Extracorporeal Membrane Oxygenation
Maria PapademetriouAff1
Ilias TachtsidisAff1
Martin J. ElliottAff2
Aparna HoskoteAff2
Clare E. ElwellAff1 Email: celwell@medphys.ucl.ac.uk
University College London, Medical Physics and Bioengineering Department, Malet Place Engineering Building, Gower Street, London, WC1E 6BT, UK
Great Ormond Street Hospital for Children, Cardiothoracic Unit, London, UK

Introduction

Extracorporeal membrane oxygenation (ECMO) is a life support system for infants with cardiorespiratory failure. Neurological complications are the largest cause of morbidity and mortality in these patients, with the reported frequency of abnormal neuroimaging ranging from 28 to 52 % [1]. Initiation of ECMO involves cannulation of the major great vessels—right common carotid artery and internal jugular vein—which may cause lateralizing cerebrovascular injury. ECMO infants suffer from hypoxia, asphyxia, and hypercarbia which can disrupt cerebral autoregulation, leaving the cerebral microcirculation vulnerable to alterations in blood pressure [2].

Methods to assess the status of autoregulation by considering the relationship between spontaneous fluctuations in MAP and cerebral blood flow (CBF) surrogates, such as (HbO2) measured by NIRS, in either the time or frequency domain using Fourier transforms were reported extensively in the literature [3]. These, conventional methods suffer from the big drawback of averaging out all the potential useful time information, hence treating cerebral autoregulation as a stationary, linear process.

Recent studies have emphasized that cerebral autoregulation is a dynamic process [4]. The continuous wavelet transform (CWT) possesses the ability to construct a time–frequency representation of a signal that offers time and frequency localization. Latka et al. used CWT to compute a synchronization index between CBF and ABP signals [5]. Wavelet cross-correlation (WCC) was introduced by Rowley et al. as the cross-correlation between CWT coefficients of two time series [6]. Spectral analysis using wavelets provides a framework for analysis of nonstationary effects in cerebral hemodynamics, thus overcoming the restrictions intrinsic to earlier methods.

Previously we used a dual-channel NIRS system and showed the presence of oscillations related to vasomotion, respiration, and heart rates [7]. Preliminary results using multichannel NIRS indicate regional variation in cerebral oxygenation [8]. Here, we investigate the use of WCC as a method to study the concordance between multisite cerebral HbO2 and mean arterial pressure in order to assess regional variations in cerebral oxygenation in neonates supported on ECMO.


Methods
2.1  Subjects and Instrumentation

A total of six veno-arterial (VA) ECMO patients, age range 1–16 days, were monitored during sequential changes in the ECMO flows. Alterations in the ECMO flows refer to successive decrease in the ECMO flow by 10 % from the initial flow, approximately every 10 min, down to 70 % of the initial flow followed by successive increase back to baseline (Fig. 28.2b).

A multichannel NIRS system (ETG-100, Hitachi Medical Ltd., Japan) was used to measure changes in oxy-(HbO2), deoxy-(HHb), and total hemoglobin (HbT) concentrations at 5 Hz. A novel cap was constructed to accommodate the optical sources and detectors (interoptode distance = 3 cm), allowing data to be collected from 12 channels. Multimodal data were collected synchronously including systemic parameters (arterial blood pressure [ABP], heart rate [HR], and arterial oxygen saturation [SpO2]) and ECMO circuit parameters (venous oxygen saturation [SvO2], arterial saturation at the cannula [SaO2]).

2.2  Data Analysis

Mean arterial pressure (MAP) was obtained by trapezoid integration of ABP every 0.2 s, equivalent to sampling frequency of 5 Hz. The time series of MAP and HbO2 were divided into sections representing each ECMO flow period (Fig. 28.3c). Each section of data was then high and low pass filtered at 0.008 and 1 Hz.

An approximate relationship between the scale α in the wavelet domain and frequency in the Fourier transform, fα, can be computed as [5]:

[Formula ID: eq1]
28.1 
fα=fcα·δt,

where fc is the center frequency and δτ is the sampling period.

Wavelet analysis was performed on HbO2 data. WCC and synchronization index, γ, were used as methods to investigate the relation between MAP and HbO2. The complex Morlet wavelet was used to calculate the CWT coefficients for MAP and HbO2 using the MatLab wavelet toolbox function cwt. The central frequency (fc) and bandwidth (fb) of the complex Morlet wavelet were both chosen as 1 in order to be in agreement with previous methods [5, 6]. A scale range with unit spacing from 5 to 100, representing frequencies 0.008–1 Hz was used to obtain two complex time series, WMAP (α,t) and WHbO2 (α,t) for each flow period A–G and across each of the 12 channels.

The WCC between MAP and HbO2 in each channel and for each flow was obtained using the equation below [6]:

[Formula ID: eq3]
28.2 
WCC¯=|RX,Y(WMAP,WHbO2α,τ)||RX,X(WMAP,α,0)·RX,X(WHbO2α,0)|,

in which RX,Y(s1, s2, α, τ) denotes the cross-correlation of the wavelet coefficients of the series s1 and s2 at a scale α and for a relative time shift τ and RX,X(s1, α, 0) denotes the autocorrelation of the time series s1 for zero time shift. WCC(α, τ) represents the cross-spectral power in the two time series (shifted relative to each other by τ) as a fraction of the total power in the two time series. WCC ranges from 0 to 1. At a given wavelet scale, WCC = 1 would indicate that the coefficients of the two wavelet transforms are related to each other by a simple scaling factor, suggesting strong synchronization at this frequency [6].

The phase difference between the two time series, MAP and HbO2 was also calculated using the circular mean, ΔΦ(α)¯, of the instantaneous phase difference between the two signals ΔΦ(α, τ) over the duration of a test segment [5]

[Formula ID: eq6]
28.3 
ΔΦ(α)¯=tan-1(∑tsin(Δϕ(α,t))∑tcos(Δϕ(α,t))).

For each time series pair at each flow period and for each channel, the maximum value of WCC(α, τ) was found within three scale bands: αi = 5 < α <20 (fαi= 0.25 Hz < fα< 1 Hz), αii = 20 < α <40 (fαii= 0.13 Hz < fα<0.25 Hz), αiii = 40 < α <80 (fαiii= 0.06 Hz < fα< 0.13 Hz). These bands were chosen to overlap with respiration rate (RR), ventilation rate (VR), and slow M-waves, respectively. The maximum circular mean, ΔΦmax, were also calculated within each scale band, for each flow period and each channel. Student’s t-test was then used to analyze the statistical significance of the differences in the group mean of each of these variables between channels.


Results

Figure 28.1 shows a set of typical WCC contours obtained from two patients at baseline flow and minimum flow. For patient 1 WCC shows no distinct peaks at baseline flows indicating no correlation between MAP and HbO2. At minimum flow, peaks in the WCC contours are shown at scales 15 (fα = 0.33 Hz), 29 (fα = 0.17 Hz) and a relatively weaker peak at scale 55 (fα = 0.09 Hz). WCC for patient 2 at baseline flow shows a relatively weak peak at a scale 34 (fα = 0.15 Hz). As with patient 1 correlation between MAP and HbO2 becomes stronger at minimum flow with the peak at scale 34 spreading to higher Mayer-waves related scales and another peak occurring at scale 10 (fα = 0.5 Hz). These peaks appear shifted from zero time lag in agreement with Rowley et al. [6].

In general, WCC between MAP and HbO2 revealed three distinct peaks within three scale regions. The first peak typically occurs at a scale of around 14 (0.36 Hz), the second at a scale around 30 (0.16 Hz) and the third at a scale around 50 (0.1 Hz). These peaks could correspond to the RR, VR, and Mayer-waves, respectively.

Figure 28.2 shows the group data for the mean of the maximum WCC, WCCmaxi, within scale band αi = 5 < α <20 ( fai=0.25 Hz<fα<1 Hz) at each flow period and across the 12 channels. By convention a value of WCC below 0.5 indicates no correlation between MAP and HbO2 [5]. A + sign indicates that HbO2 lags MAP, i.e. ΔΦ> 0, only for WCC > 0.5. A − sign is used to indicate that HbO2 is leading MAP, i.e. ΔΦ < 0, where WCC > 0.5. There are statistically significant differences (p < 0.05) in mean WCCmaxi across all flows between symmetrical channels most likely positioned on the right and left parietal lobes (Fig. 28.3d). WCCmaxi for all flows in the three channels positioned on the left parietal lobe is below 0.5 suggesting no correlation between MAP and HbO2 in these channels. A general increase in WCCmaxi was observed with decrease in flow across all channels. WCCmaxi is highest either at flow period E or F. αmaxi across flow changes for all channels ranges from 9 to 17 (0.29–0.56 Hz) (Fig. 28.3e). Most of the channels show a shift in αmaxi to a lower scale when the highest WCCmaxi is reached (flow period E).


Discussion and Conclusions

WCC between HbO2 and MAP provides a useful method to investigate the dynamics of cerebral autoregulation. Cerebral autoregulation on ECMO is poorly studied, since there have been no easy noninvasive methods to study and interpret complex cerebral physiological process. Our results showed a relationship between WCC and ECMO flow in the grouped data of six patients. These differences were statistically significant between right and left hemispheres, especially when the flows were weaned sequentially. Modest manipulations of ECMO flows are associated with regional changes in cerebral autoregulation which may potentially have an important bearing on clinical outcome.


Acknowledgment

This work was supported by Hitachi Medical Ltd., Japan.


References
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2. Liem D,Hopman J,Oeseburg B,de Haan A,Festen C,Kollee L. Year: 1995Cerebral oxygenation and hemodynamics during induction of extracorporeal membrane oxygenation as investigated by near infrared spectrophotometryPediatrics955555617700758
3. Czosnyka M,Brady K,Reinhard M,Smielewski P,Steiner L. Year: 2009Monitoring cerebrovascular autoregulation: facts, myths and missing linksNeurocrit Care103738619127448
4. Panerai RB,Moody M,Eames PJ,Potter JF. Year: 2005Cerebral blood flow velocity during mental activation: interpretation with different models of the passive pressure–velocity relationshipJ Appl Physiol992352236216099892
5. Latka M,Turalska M,Latka MG,Kolodziej W,Latka D,West BJ. Year: 2005Phase dynamics in cerebral autoregulationAm J Heart Circ Physiol289H2272H2279
6. Rowley AB,Payne SJ,Tachtsidis I,Ebden MJ,Whiteley JP,Gavaghan DJ,Smith M,Elwell CE,Delpy DT. Year: 2007Synchronisation between arterial blood pressure and cerebral concentration investigated by wavelet cross-correlationPhysiol Meas2816117317237588
7. Papademetriou MD,Tachtsidis I,Leung TS,Elliott MJ,Hoskote A,Elwell CE. Year: 2010Cerebral and peripheral tissue oxygenation in infants and children supported on ECMO for cardio-respiratory failureAdv Exp Med Biol66244745320204828
8. Papademetriou MD,Tachtsidis I,Leung TS,Elliott MJ,Hoskote A,Elwell CE. Year: 2011Regional cerebral oxygenation measured by multichannel near-infrared spectroscopy (optical topography) in an infant supported on venoarterial extracorporeal membrane oxygenationJ Thorac Cardiovasc Surg141e31e3321334016

Figures

[Figure ID: f2]
Fig. 28.2  Group WCC on between MAP and HbO2 within scale band αi = 5 <α < 20 (fai = 0.25 Hz <fα < 1 Hz)

(a) channel arrangement; (b) sequence of flow changes (A = 100 %, B = 90 %, C = 80 %, D = 70 %, E = 80 %, F = 90 %, G = 100 %); (c) WCCmaxi at all flow periods across all channels; (d) mean WCCmaxi across all flow periods of channels on the right side and symmetrical channels on the left side; (e) mean of scale at WCCmaxi,αmaxi, for each flow period across all channels. +/− denotes HbO2 lagging/leading MAP for WCCmaxi>0.5. (asterisk) Statistical significant difference between symmetrical channels on right and left hemispheres (p<0.05)



[Figure ID: f1]
Fig. 28.1  Wavelet cross-correlation (WCC) between MAP and HbO2 for two ECMO patients

Low correlation is shown at baseline ECMO flows (a and c) and high correlation around scales 16 and 30 for patient 1 at minimum flow (b) and around scales 16 and 40–80 for patient 2 (d) at minimum flow



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