braccia - Physics at Lancaster University

physics.lancs.ac.uk

braccia - Physics at Lancaster University

Outline

Brain, Respiration and Cardiovascular

Causalities in Anæsthesia

BRACCIA

Lawrence Sheppard 1 on behalf of Aneta Stefanovska 2

1 BRACCIA Scientific Secretary

2 BRACCIA Scientific Coodinator

GIACS, Torino, 27 October 2005

Sheppard & Stefanovska

BRACCIA


Outline

Outline

1 Introduction

Complexity

Measurements

Analysis

2 Anæsthesia

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

3 BRACCIA

A NEST applying ideas

from complex systems

to medicine.

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Complexity and human body

Complexity

Measurements

Analysis

Human body + brain, most complex mechanism ever to

have existed –

10 13 cells, 10 11 neurons in brain.

40000 kilometres of capillaries.

Mostly self-repairing, long-lasting.

E.g. heart lasts for > 2 gigabeats.

Complex signals from physiological measurements.

Challenges: (i) to understand the

underlying processes by analysis of

the signals; and (ii) to exploit this

knowledge.

Sheppard & Stefanovska

BRACCIA


Measured quantities

Introduction

Anæsthesia

BRACCIA

Complexity

Measurements

Analysis

Make simultaneous measurements of several different

physiological quantities, e.g. –

Cardiac activity (from ECG)

Respiration (belt+sensor on thorax)

Blood pressure (sensor on finger)

Blood flow rate (laser-Doppler flowmetry)

Temperature (semiconductor sensor)

Brain waves (EEG)

Typically, data are complex and oscillatory.

Data recorded over 30 minutes,

noninvasively, and usually for a

quiescent subject.

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Complexity

Measurements

Analysis

Physiological signals are complex

Respiration

ECG

Amplitude

HRV

Blood pressure

Blood flow 1

Note –

“HRV” is derived from ECG

Several frequencies evident

in blood flow...

Blood flow 2

0 5 10 15 20 25

Time (s)

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Averaged wavelet spectra

Complexity

Measurements

Analysis

Average wavelet transform

Respiration

ECG

0.011 Hz

0.026 Hz

HRV

BP

0.014 Hz

0.03 Hz

BF-1

BF-2

0.012 Hz

0.09 Hz

0.11 Hz

0.18 Hz

0.18 Hz

0.18 Hz

0.36 Hz

0.032 Hz

0.091 Hz

0.18 Hz

1.1 Hz

1.1 Hz

0.01 0.03 0.1 0.3 1

Frequency (Hz)

1.1 Hz

1.1 Hz

Note –

Log frequency resolution

Same spectral peaks in

all data?

Peaks are broad

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Complexity

Measurements

Analysis

Time–frequency wavelet transform

Time variation of maxima –

Amplitudes

Wavelet transform (AU)

250

200

150

100

50

0

0.1

0.3

Frequency (Hz)

1

3

540

570

560

550

Time (s)

Frequency (Hz)

200

150

Frequencies

100

540 545 550 555 560 565 570

Amplitude (AU)250

1.15

1.1

1.05

1

540 545 550 555 560 565 570

Time (s)

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Complexity

Measurements

Analysis

Physiological origins of the rhythms?

∼Hz

Process

1.0 Heart – obvious

0.2 Respiration – obvious?

0.1 Myogenic activity of smooth muscle – same in vitro

0.04 Neurogenic activity – absent after denervation

0.01 Endothelial vasodilation – NO dependent

(0.007) (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...

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Complexity

Measurements

Analysis

Evidence of inter-oscillator couplings?

Modulation

Synchronization

Frequency (Hz)

200

150

100

540 545 550 555 560 565 570

Amplitude (AU)250

1.15

1.1

1.05

1

540 545 550 555 560 565 570

Time (s)

Ψ 1

f /f h r

1

0.8

0.6

0.4

0.2

0

0 100 200 300 400 500 600 700 800

Time (s)

10

8

6

0 100 200 300 400 500 600 700 800

Time (s)

Closely analogous to coupled-oscillator phenomena seen

elsewhere in physical science and engineering.

Relative values of oscillator amplitudes and couplings

reflect state of the system, so...

Maybe there are changes with depth of anæsthesia?

Sheppard & Stefanovska

BRACCIA


Anæsthesia

Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

A chemical perturbation of the organism results in a

temporary loss of consciousness.

Mysterious – mind/body relationship etc.

Certain nervous pathways blocked.

Important – needed for surgery.

“...the anæsthetist is still unable to measure the depth

of anæsthesia in order to prevent inadvertent awakening

during anæsthesia.”

C.J.D. Pomfrett, Brit. J. Anæsthesia, 1999.

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Incidence of awareness in anæsthesia

Author Date Sample Awareness %

Hutchinson 1960 656 1.2

Harris 1971 120 1.6

McKenna 1973 200 1.5

Wilson 1975 490 0.8

Liu et al 1990 1000 0.2

Lennmarken & Sandin 2004 1238 0.9

Too much anæsthetic ⇒ bad for patient.

Too little anæsthetic ⇒ awareness.

So need a reliable measure of depth of anæsthesia.

Can coupled oscillator picture help?

Sheppard & Stefanovska

BRACCIA


Rats

Introduction

Anæsthesia

BRACCIA

Ψ 1

Practice & problems

2πAnæsthesia in rats

Anæsthesia in humans

π

Initially, study rats.

a Administer anæsthetic;

b Measure;

c Anæsthesia lasts

∼100 min.

Synchronisation effects

are much stronger in

anæsthesia.

Starts wearing off after

typically 40–50 min.

Can also calculate direction

of the coupling...

λ 1,2

λ 1,3

λ 1,4

λ 1,5

Sheppard & Stefanovska

0

1

0.5

0

1

0.5

0

1

0.5

0

1

0.5

0

0 10 20 30 40

BRACCIA

Time (min)


Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Cardio-respiratory directionality in rats

d r,c

d r,c

1

0

-1

0.4

0.2

0

-0.2

0 20 40 60

Time [min]

Musizza et al, to be published

a

b

Technique of

Rosenblum et al

PRE 65, 041909

(2002)

Technique of

Paluš & Stefanovska

PRE 67, 055201

(2003)

NB Deep → light anæsthesia transition at ∼40 minutes.

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Cardio-respiratory-EEG in rats

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Use wavelet transform to

follow frequency

evolution of different

EEG waves.

Marked decrease occurs

in δ–wave amplitude, at

deep-light anæsthesia

transition.

Increase in γ-wave,

θ-wave, frequencies.

f [Hz] f [Hz] f [Hz]




50

39

30

7.5

4.5

2.5

3.5

1.5

0.5

0 10 20 30 40 50 60 70

Time [min]

1.7

0.01

332

6.8

1185

15.6

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Cardio-respiratory-δ directionality, in rats

Definite changes in

pattern of interaction

at deep→shallow

anæsthesia

transition.

Mainly qualitative at

present.

R

**

*

DEEP

a

***

0


0

C

0

**

**

R

(Musizza et al, to be published)

LIGHT

Current techniques adversely affected by strong noise

(phase dynamics), or where the frequency ratio becomes

large (information theory).

b

0

*


0

C

0

Sheppard & Stefanovska

BRACCIA


Are humans like rats?

Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Anæsthetised humans – preliminary study

To see if changes in synchronization phenomena, like those in

rats, also occur in humans – with Andy Smith, Mike Entwistle,

Bojan Musizza, Andriy Bandrivskyy –

Study volunteers – healthy males aged 25–40.

Measure for 30 minutes prior to minor surgery in Royal

Lancaster Infirmary.

Record ECG, respiration, EEG (blood pressure and

temperature).

Look for evidence of cardio-respiratory-cortical

synchronization, and information about coupling

directions...

Compare with measurements on wakeful humans.

Sheppard & Stefanovska

BRACCIA


d

d

Introduction

Anæsthesia

BRACCIA

Anæsthetised v. wakeful human

Cardiac⇒

f c

Hz

1.8

1.6

1.4

1.2

Anæsthetised

Anaesthesia subject 6

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

f c

Hz

1.4

1.2

1

0.8

0.6

Wakeful

No Anaesthesia Andriy

Respiration⇒

f r

Hz

0.4

0.2

0

f r

Hz

0.4

0.2

Ratio⇒

f c

/ f r

30

20

10

f c

/ f r

6

4

6

6

S-gramme⇒

Ψ

4

2

Ψ

4

2

λ 1,7

0

1

0.5

200 400 600 800 1000 1200 1400 1600 1800

λ 1,3

0

1

0.5

200 400 600 800 1000 1200 1400 1600 1800

0

1

0

1

S-indices⇒

λ λ 1,9 1,8

0.5

0

1

0.5

λ 1,4

λ 1,5

0.5

0

1

0.5

Couplings⇒

Directions⇒

c 1

, c 2

λ 1,10

0

1

0.5

0

0.4

0.2

0

1

0

−1

200 400 600 800 1000 1200 1400 1600 1800

time, s

λ 1,6

c 1

, c 2

0

1

0.5

0

0.3

0.2

0.1

0

1

0

−1

200 400 600 800 1000 1200 1400 1600 1800

time, s

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Initial human anæsthesia data

Practice & problems

Anæsthesia in rats

Anæsthesia in humans

Humans rather like rats, in that

Cardiorespiratory synchronisation much stronger than in

waking state.

Respiration drives heart in deep anæsthesia.

Note (i) have not yet examined the same subject in both

anæsthetised and wakeful states, (ii) only steady

anæsthesia investigated to date, (iii) have not yet

examined transition to light anæsthesia or wakefulness.

But there is clear potential for basis of new kind of

depth-of-anaesthesia monitor...

Research going forward under BRACCIA,

started 1 June 2005.

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

NEST with 8 teams, broad scientific expertise

Team leader Institute Expertise

Stefanovska Universities of Ljubljana & Nonlinear dynamics

Lancaster

McClintock Lancaster University Stochastic dynamics

Smith Royal Lancaster Infirmary Clinical anæsthesia

Hasler Swiss Federal Inst of Tech, Networks

Lausanne

Ribarič University of Ljubljana Neurobiology, rats

Raeder Ulleval Hospital, Oslo Clinical anæsthesia

Kurths Potsdam, Inst of Complex Synchronisation

Systems

Paluš Prague, Inst of Computer

Sciences

Information theory

Sheppard & Stefanovska

BRACCIA


BRACCIA

Introduction

Anæsthesia

BRACCIA

START

WP2: Methodology: development

of methods for testing causalities

(synchornization, inter-oscillators

couplings, directionality,

surrogates)

Lead contractor: CO1, CR2,

CR7, CR8

WP3: Methodology: measurement

setups for simultaneous recording

of EEG, ECG, respiration, skin

temperature and blood pressure

Lead contractor: CO1, CR2

The plan. . .

WP1: Project management

Lead contractor: CO1

WP5: Measurements

on rats

WP4: Measurements Lead contractor:

on humans setups CO1, CR5

for simultaneous

recording of EEG,

ECG, respiration,

skin temperature and

blood pressure

Lead contractor:

CO1, CR2, CR3,

CR6

WP8: Modeling cardiovascular

interactions using up to five

coupled oscillators

WP9: Modeling brain

Lead contractor: CO1, CR2,

waves using a large

CR7

ensemble of

oscillators

Lead contractor:

CO1, CR2, CR4, CR7

WP11: Summary and planning

the protocol for a large-scale

multi-centre study

Lead contractor: all groups

WP6: Analysis of

measured timeseries

Lead contractor:

CO1, CR2, CR4,

CR7, CR8

WP7: Neurological,

physiological and

clinical

interpretation of

obtained results

Lead contractor:

all groups

WP10: Modeling

interactions on micro

and macroscopic level:

between oscillators in

the brain, the cardiac

and respiratory

oscillators

Lead contractor: CO1,

CR2, CR4, CR7

WP1: Project management

Lead contractor: CO1

WP12: Dissemination

Lead contractor: all groups

END

Sheppard & Stefanovska

BRACCIA


Introduction

Anæsthesia

BRACCIA

Acknowledgements & References

Acknowledgements

We are indebted to ARRS (Slovenia), EPSRC (UK), Wellcome

Trust, INTAS, and the Leverhulme Trust for funding our earlier

research, and to the EC for a NEST contract supporting

BRACCIA from 1 June 2005.

Selected references

1. A Stefanovska & M Bračič, “Physics of the human cardiovascular

system”, Contemporary Phys. 40, 31–55 (1999).

2. A Stefanovska, H Haken, P V E McClintock, M Hožič, F Bajrović, and S

Ribarič, “Reversible transitions between synchronization states of the

cardio-respiratory system”, Phys. Rev. Lett. 85, 4831–4834 (2000).

3. V N Smelyanskiy, D G Luchinsky, A Stefanovska, and P V E McClintock,

“Inference of a nonlinear stochastic model of the cardiorespiratory

interaction”, Phys. Rev. Lett. 94, 098101 (2005).

Sheppard & Stefanovska

BRACCIA

More magazines by this user
Similar magazines