Transcript TS1 - Cerebral AutoRegulation Network

Multimodal Pressure-Flow Analysis to
Assess Dynamic Cerebral Autoregulation
Albert C. Yang, MD, PhD
Attending Physician, Department of Psychiatry,
Taipei Veterans General Hospital, Taipei, Taiwan
Assistant Professor, School of Medicine,
National Yang-Ming University, Taipei, Taiwan
[email protected]
Overview

What is cerebral autoregulation and how to
measure it?

Multimodal pressure-flow analysis

Empirical Mode Decomposition and Hilbert-Huang
Transform

Subsequent improvement

Demonstration
Body as Servo-Mechansim Type Machine
• Importance of corrective mechanisms to
keep variables “in bounds.”
• Healthy systems are self-regulated to
reduce variability and maintain
physiologic constancy.
Perturbation
Baseline
Restored steady state
Underlying notion of “constant,” “steady-state,” conditions.
Walter Cannon 1929
Ideal Cerebral Autoregulation
Lassen NA. Physiol Rev. 1959;39:183-238
Strandgaard S, Paulson OB. Stroke.1984;15:413-416
Static Autoregulation Measurement
Tiecks FP et al., Stroke. 1995; 26: 1014-1019
Dynamic Autoregulation Measurement
Tiecks FP et al., Stroke. 1995; 26: 1014-1019
Autoregulation Index
Tiecks FP et al., Stroke. 1995; 26: 1014-1019
Challenges of Cerebral
Autoregulation Assessment
• Blood pressure and cerebral blood flow
velocity are often nonstationary and their
interactions are nonlinear.
• Need a new method that can analyze
nonlinear and nonstationary signals.
Novak V et al., Biomed Eng Online. 2004;3(1):39
Multimodal Pressure-Flow Analysis
Participants

15 normotensive healthy subjects


20 hypertensive subjects


age 40.2 ± 2.0 years
age 49.9 ± 2.0 years
15 minor stroke subjects


18.3 ± 4.5 months after acute onset
age 53.1 ± 1.6 years
Novak V et al., Biomed Eng Online. 2004;3(1):39
Measurements

Blood pressure


Finger Photoplethysmographic Volume Clamp
Method.
Blood flow velocities (BFV) from bilateral middle
cerebral arteries (MCA)

Transcranial Doppler Ultrasound.
Novak V et al., Biomed Eng Online. 2004;3(1):39
Valsalva Maneuver
Arterial Blood Pressure
HHT residual
180
I. Expiration - 160
mechanical
IV
I
mmHg
140
IV. increased cardiac
output and increased
peripheral resistance
120
100
80
III
60
II. reduced venous return,
BP falls 40
20
30
40
III. Inspiration - mechanical
II
50
Time (sec)
60
70
80
Valsalva Maneuver Dynamics
Blood Pressure
Blood Flow Velocity – Right Middle Cerebral Artery
Blood Flow Velocity – Left Middle Cerebral Artery
Empirical Mode Decomposition (EMD)
黃 鍔 院士
Norden E. Huang

The Empirical Mode Decomposition
Method and the Hilbert Spectrum for
Non-stationary Time Series Analysis,
(1998) Proc. Roy. Soc. London, A454,
903-995.

The motivation of EMD development was
to solve the problems of non-linearity and
non-stationarity of the data

Is an adaptive-based method
Cited 7,722 Times!
Empirical Mode Decomposition
Huang et al. Proc Roy Soc Lond A 1998;454:903-995.
Empirical Mode Decomposition
Step 1: Find the envelope alone local maximum and minimum
Huang et al. Proc Roy Soc Lond A 1998;454:903-995.
Empirical Mode Decomposition
Step 2: Find the average between envelopes
Huang et al. Proc Roy Soc Lond A 1998;454:903-995.
Empirical Mode Decomposition
Intrinsic Mode Function
Step 3: To determine the fluctuation of original signal
around the average of envelopes
Huang et al. Proc Roy Soc Lond A 1998;454:903-995.
Empirical Mode Decomposition
Sifting : to get all IMF components
x( t )  c1  r1 ,
r1  c2  r2 ,
. . .
rn  1  cn  rn .
 x( t ) 
n
c
j 1
j
 rn .
Huang et al. Proc Roy Soc Lond A 1998;454:903-995.
Original Data
Empirical Mode Decomposition
A Simple Example
2
0
-2
0
10
20
30
40
50
60
0
10
20
30
40
50
60
0
10
20
30
40
50
60
0
10
20
30
40
50
60
IMF 1
1
0
-1
IMF 2
1
0
IMF 3
-1
0.5
0
-0.5
Empirical Mode
Decomposition
Original blood pressure
waveform
Key mode of blood
pressure waveform
during Valsalva
maneuver
Blood Pressure versus Blood Flow Velocity
Temporal (time) Relationship
Novak V et al., Biomed Eng Online. 2004;3(1):39
Blood Pressure versus Blood Flow Velocity
Phase Relationship
Control
Stroke
Novak V et al., Biomed Eng Online. 2004;3(1):39
Between Groups Phase Comparisons
*** p < 0.005,
** p < 0.01
Groups BPR Values Comparisons
+++ p <0.001
Conventional Autoregulation Indices
Novak V et al., Biomed Eng Online. 2004;3(1):39
Summary: Original Version of
MMPF Analysis

Regulation of BP-BFV dynamics is altered in
both hemispheres in hypertension and stroke,
rendering BFV dependent on BP.

The MMPF method provides high time and
frequency resolution.

This method may be useful as a measure of
cerebral autoregulation for short and
nonstationary time series.
Limitations: Original Version
of MMPF Analysis

Requires visual identification of key mode of
physiologic time series

Mode mixing with original EMD analysis

Valsalva maneuver itself has certain risk
Subsequent Improvements of
MMPF Analysis

Use Ensemble EMD (EEMD) Analysis
Wu, Z., et al. (2007) Proc. Natl. Acad. Sci. USA., 104, 14889-14894

Resting-state MMPF Analysis
K. Hu, et al., (2008) Cardiovascular Engineering

Selection of key mode related to respiration
during resting-state condition
M-T Lo, k Hu et al., (2008) EURASIP Journal on Advances in Signal Processing

Comparison of phase shifts in multiple time
scales
Hu K et al., (2012) PLoS Comput Biol 8(7): e1002601

Implementation and automation of the method
Dr. Yanhui Liu. DynaDx Corp. U.S.A.
Resting-State Multimodal
Pressure-Flow Analysis
K. Hu, et al., Cardiovascular Engineering, 2008.
Respiratory Signals From
Blood Pressure Time Series
M-T Lo, k Hu et al., EURASIP Journal on Advances in Signal Processing, 2008
Resting-State Multimodal
Pressure-Flow Analysis
Resting-State Multimodal
Pressure-Flow Analysis
Cerebral Blood Flow Regulation at
Multiple Time Scales
Hu K et al., PLoS Comput Biol 2012; 8(7): e1002601
Traumatic Brain Injury and
Cerebral Autoregulation
k. Hu, M-T Lo et al., journal of neurotrauma, 2009
Traumatic Brain Injury and
Cerebral Autoregulation
k. Hu, M-T Lo et al., journal of neurotrauma, 2009
Midline Shift Correlates to LeftRight Difference in Autoregulation
k. Hu, M-T Lo et al., journal of neurotrauma, 2009
Resources

Empirical Mode Decomposition (Matlab)


http://rcada.ncu.edu.tw/research1.htm
DataDemon (Generic Analysis Platform)

For 64-bit system,
https://dl.dropbox.com/u/7955307/daily_build/x64/Data
DemonSetupPRO.msi

For 32-bit system,
https://dl.dropbox.com/u/7955307/daily_build/x86/Data
DemonSetupPRO.msi
Acknowledgements
Vera Novak, MD, PhD
Yanhui Liu, PhD
Chung-Kang Peng, PhD
Kun Hu, PhD
Albert C. Yang, MD, PhD
Ment-Zung Lo, PhD