brain, respiration, and cardiac causalities in anæsthesia · 2016-04-15 · brain, respiration,...
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Brain, Respiration, and CardiacCausalities in Anæsthesia
Origins, basis, rationale and science
Peter V. E. McClintock1, Martin Hasler2, Juergen Kurths3,Per Kvandal4, Svein Landsverk4,
Dmitri G. Luchinsky1, Milan Paluš5, Arkady Pikovsky3,Zvezdan Pirtošek6, Johan C. Ræder4,
Samo Ribaric7, Michael Rosenblum3, Andrew F. Smith8,Aneta Stefanovska1,9, and Niels Wessel3
1Lancaster, 2Lausanne, 3Potsdam, 4Oslo, 5Prague, 6Ljubljana UMC,7Ljubljana FM, 8Lancaster RLI, 9Llubljana FEE
Lancaster – 12 April 2016BRACCIA Consortium The BRACCIA enterprise
Outline
1 IntroductionPhysiological oscilllationsAnæsthesiaData analysis methods
2 The BRACCIA enterpriseConsortiumResearch programme
3 ConclusionSummary
How do the oscillations and their couplings changein anæsthesia? Might these changes provide anovel basis for measuring depth of anæsthesia?
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Averaged wavelet spectra
0.18 Hz
1.1 Hz
0.18 Hz
0.36 Hz0.18 Hz0.09 Hz
0.026 Hz0.011 Hz
1.1 Hz
Ave
rage
wav
elet
tran
sfor
m
1.1 Hz0.11 Hz0.03 Hz
0.014 Hz
0.01 0.03 0.1 0.3 1
1.1 Hz0.18 Hz0.091 Hz0.032 Hz0.012 Hz
Frequency (Hz)
Respiration
ECG
HRV
BP
BF-1
BF-2
Note –
Log frequency resolution
Same spectral peaks inall data?
Peaks are broadened bytheir time-variations.
The body is “humming” withrhythms! But where do theycome from?
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Physiological origins of the rhythms?
∼Hz Process1.0 Heart – obvious0.2 Respiration – obvious?0.1 Myogenic activity of smooth muscle – same in vitro0.04 Neurogenic activity – absent after denervation0.01 Endothelial vasodilation – NO-dependent0.007 Endothelial vasodilation – endothelin-dependent
Our interest centres especially on –
Relative amplitudes
Couplings
between respiratory, cardiac, and cortical (EEG) oscillations,which seem to reflect the state of the organism...
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Anæsthesia – practice & problems
What is (general) Anæsthesia?
A chemical perturbation of the organism resulting in atemporary loss of consciousness.
Mysterious – mind/body relationship etc.
Certain nervous pathways blocked.
Important – needed for surgery.
“...the anæsthetist is still unable to measure the depthof anæsthesia in order to prevent inadvertent awakeningduring anæsthesia.”
C.J.D. Pomfrett, Brit. J. Anæsthesia, 1999.
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Incidence of awareness in anæsthesia
Too much anæsthetic ⇒ bad for patient.Too little anæsthetic ⇒ awareness.
Author Date Sample Awareness %Hutchinson 1960 656 1.2Harris 1971 120 1.6McKenna 1973 200 1.5Wilson 1975 490 0.8Liu et al 1990 1,000 0.2Lennmarken & Sandin 2004 1,238 0.9R. Coll. of Anæsth. (NAP5) 2014 3,000,000 0.005
So need a reliable measure of depth of anæsthesia.Can physics (of coupled oscillators) help? Maybe...
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Example – cardio-respiratory synchronisation in rats
Initially, studied rats.
a Administer anæsthetic;b Measure heart-rate
& respiration;c Anæsthesia lasts
about 100 min.
Synchronisation effectsare much stronger duringanæsthesia.
Starts wearing off aftertypically 40–50 min.
Depth of anæsthesia relatedto synchronisation ratio.
[Stefanovska et al., Phys. Rev. Lett. 85, 4831 (2000).]
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BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Aims and challenges
Interested in how to characterise signals so as to revealchanges in anæsthesia. Challenges include –
Numerous signals are potentially relevant – so which willbe most useful?
Inherent time-variations of the signals – how toaccommodate or exploit them?
How best to combine the information coming from manydifferent analyses?
New analysis methods needed to be developed beforeBRACCIA could come to fruition.
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Data analysis methods
Wavelet analysis, the main “work horse”, provides –
Time-frequency information with logarithmic frequency resolutionPower in different spectral ranges
Frequency variability analysis – measured directly from ECG R-peaksand respiration signal maxima, giving HRV and RFV.
Wavelet phase coherence analysis – apply to any pair of signals. Ifphase coherence persists over time then –
Either the signals have a common sourceOr they are synchronised
Coupling function analysis – apply to any pair of signals. Extractsdetailed quantitative information about the mutual interactions betweenthe oscillators.
Result: a large number of different measures of the state, e.g. averages,powers in different spectral ranges for several signals, HRV, RFV, coherencesat different frequencies between different signals, coupling functions...
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
Physiological oscilllationsAnæsthesiaData analysis methods
Making optimal use of the results – classification
Problem is how best to combine the diverse measures (attributes) ofstate, e.g. awake, or anaesthetised with propofol, or with sevoflurane.
Use distance-based classification and nearest neighbour classifier.
With an appropriate distance measure in the multi-dimensional space,the three states form separated clusters.
Leads to a confusion matrix giving the likelihood of correct and incorrectclassification for subjects in each group.
Using a learning algorithm, the classification accuracy improves withnumber of subjects.
[Kenwright et al, Anæsthesia 70 1356–1368 (2015), Suppl. Mat.]
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IntroductionThe BRACCIA enterprise
Conclusion
ConsortiumResearch programme
Proposed BRACCIA Consortium
No. Organisation Abbreviation Town Country1 University of Ljubljana, UNILJFE Ljubljana Slovenia
Faculty of Elec. Eng.2 Lancaster University UNILANCS Lancaster UK3 Royal Lancaster Infirmary MBHT Lancaster UK4 Swiss Federal Inst, of Tech. EPFL Lausanne Switzerland5 University of Ljubljana, UNILJFM Ljubljana Slovenia
Faculty of Medicine6 University of Oslo UOUH Oslo Norway
Ulleval Hospital7 University of Potsdam UP Potsdam Germany
Inst. of Complex Systems8 Academy of Sciences ICSASCR Prague Czech Repub.
Inst. of Computer Sciences
Coordinator: Aneta Sefanovska
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
ConsortiumResearch programme
Measurements
The simultaneous measurements included time series of –
Electrical activity of the heart (ECG)
Respiratory activity
Skin temperature
Skin conductivity
Brain activity (EEG)
For patients (a) awake, (b) anæsthetised with propofol, (c)anæsthetised with sevoflurane.
All measurements used the Cardo&BrainSignals signalconditioning unit specially designed for BRACCIA by JozefStefan Institute (Ljubljana).
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
ConsortiumResearch programme
The Cardo&BrainSignals signal conditioning unit
Physiologicaltransducer
Signalconditioning
Anti-aliasingLP filter
DS ADCDecimationLP filter
Log fileF : 9600 HzOversamp. rat.: 128Resolution: 24-bitLP FIR filt. F =4.9 kHz
s
0
Fs: 1200 Hz
Main features –
12 identical channels
Synchronized parallel operation of ∆Σ ADCs
24-bit A/D conversion
9.6 kHz sampling frequency
Optical interface, to reduce interference
Same measurement set-up in both hospitals, as well as forhealthy subjects (Lancaster) and rats (Ljubljana).
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
ConsortiumResearch programme
Measurements
Channel Signal1 ECG2 EEG-13 EEG-24 EEG-35 EEG-46 BP Pulse7 Respiration8 Conductivity9 Temperature 1
10 Temperature 211 Gen. purpose 112 Gen. purpose 2
All recorded (i) without and(ii) with anæsthesia.
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650 655 660 665 670 6750
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Time [s]
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
Conclusion
ConsortiumResearch programme
BRACCIA Project Plan
Logistics
Measurements in Oslo,Lancaster, Ljubljana.
Resultant time seriesuploaded to database inLancaster.
Data downloaded toLancaster, Lausanne,Potsdam, Prague, foranalysis.
Joint, collaborativeinterpretation
Schedule
Proposal 2005-2008Extended 2005-2009Actual 2005-2016...
WP3:
Lead contractor:
Methodology: measurementsetups for simultaneous recordingof EEG, ECG, respiration, skintemperature and blood pressure
CO1, CR2
WP4:
Lead contractor:
Measurementson humans setupsfor simultaneousrecording of EEG,ECG, respiration,skin temperature andblood pressure
CO1, CR2, CR3,CR6
WP12:
Lead contractor:
Disseminationall groups
WP11:
Lead contractor:
Summary and planningthe protocol for a large-scalemulti-centre study
all groups
WP9:
Lead contractor:
Modeling brainwaves using a largeensemble ofoscillators
CO1, CR2, CR4, CR7
WP8:
Lead contractor:
Modeling cardiovascularinteractions using up to fivecoupled oscillators
CO1, CR2,CR7
WP7:
Lead contractor:
Neurological,physiological andclinicalinterpretation ofobtained results
all groups
WP6:
Lead contractor:
Analysis ofmeasured time-series
CO1, CR2, CR4,CR7, CR8
WP5:
Lead contractor:
Measurementson rats
CO1, CR5
WP
1:
Le
ad
co
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Pro
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ma
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WP2:
Lead contractor:
Methodology: developmentof methods for testing causalities(synchornization, inter-oscillatorscouplings, directionality,surrogates)
CO1, CR2,CR7, CR8
START
END
WP10:
Lead contractor:
Modelinginteractions on microand macroscopic level:between oscillators inthe brain, the cardiacand respiratoryoscillators
CO1,CR2, CR4, CR7
WP
1:
Le
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co
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Pro
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ma
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BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
ConclusionSummary
Summary – not including outcomes
Physiological oscillations carry information about the stateof the organism.
Powerful time series data analysis methods are nowavailable to analyse the oscillations.
BRACCIA uses these to explore whether the oscillationscan quantify depth of anæsthesia.
Following about 11 years of work, the BRACCIA enterpriseis coming to fruition and a clear answer is now emerging...
...to be reported in the presentations by Andy Smith andJohan Ræder!
BRACCIA Consortium The BRACCIA enterprise
IntroductionThe BRACCIA enterprise
ConclusionSummary
Acknowledgements and recent publications
AcknowledgementsWe are grateful to the entire BRACCIA team for their collaboration, and toEuropean Community (FP6), the Engineering and Physical SciencesResearch Council (UK) and ARRS (Slovenia) for funding the research.
Recent BRACCIA publications1 D A Kenwright, Bernjak, T Draegni, S Dzeroski, M Entwistle, M Horvat,
P Kvandal, S A Landsverk, P V E McClintock, B Musizza, J Petrovcic, JRæder, L W Sheppard, A F Smith, T Stankovski and A Stefanovska,“The discriminatory value of cardiorespiratory interactions indistinguishing awake from anæsthetised states: a randomisedobservational study”, Anæsthesia 70 1356–1368 (2015).
2 T Stankovski, S Petkoski, J Ræder, A F Smith, P V E McClintock, and AStefanovska, “Alterations in the coupling functions between cortical andcardio-respiratory oscillations due to anæsthesia with propofol andsevoflurane”, Phil. Trans. R. Soc. A 374, 20150186 (2016).
BRACCIA Consortium The BRACCIA enterprise