Multimodal Pressure-Flow Analysis to Assess Multimodal Pressure-Flow Analysis to Assess Dynamic Cerebral AutoregulationDynamic Cerebral Autoregulation

ccyang@physionet.org

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

OverviewOverview What is cerebral autoregulation and how to

measure it?

Multimodal pressure-flow analysis Empirical Mode Decomposition and Hilbert-Huang

Transform

Subsequent improvement

Demonstration

Restored steady state Baseline

Perturbation

Body as Servo-Mechansim Type MachineBody 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.

Underlying notion of “constant,” “steady-state,” conditions.

Walter Cannon 1929

Ideal Cerebral AutoregulationIdeal Cerebral Autoregulation

Lassen NA. Physiol Rev. 1959;39:183-238Strandgaard S, Paulson OB. Stroke.1984;15:413-416

Static Autoregulation MeasurementStatic Autoregulation Measurement

Tiecks FP et al., Stroke. 1995; 26: 1014-1019

Dynamic Autoregulation MeasurementDynamic Autoregulation Measurement

Tiecks FP et al., Stroke. 1995; 26: 1014-1019

Autoregulation IndexAutoregulation Index

Tiecks FP et al., Stroke. 1995; 26: 1014-1019

Challenges of Cerebral Challenges of Cerebral Autoregulation AssessmentAutoregulation 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 AnalysisMultimodal Pressure-Flow Analysis

ParticipantsParticipants 15 normotensive healthy subjects

age 40.2 ± 2.0 years

20 hypertensive subjects 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

MeasurementsMeasurements 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 ManeuverValsalva Maneuver

Time (sec)

20 30 40 50 60 70 80

mm

Hg

40

60

80

100

120

140

160

180

Arterial Blood PressureHHT residual

II

I

III

IVI. Expiration - mechanicalI. Expiration - mechanical

II. reduced venous return, BP falls

II. reduced venous return, BP falls

III. Inspiration - mechanicalIII. Inspiration - mechanical

IV. increased cardiac output and increased peripheral resistance

IV. increased cardiac output and increased peripheral resistance

Valsalva Maneuver DynamicsValsalva Maneuver Dynamics

Blood Pressure

Blood Flow Velocity – Right Middle Cerebral Artery

Blood Flow Velocity – Left Middle Cerebral Artery

Empirical Mode Decomposition (EMD)Empirical Mode Decomposition (EMD)

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

黃 鍔 院士Norden E. Huang

Cited 7,722 Times!

Empirical Mode DecompositionEmpirical Mode Decomposition

Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

Empirical Mode DecompositionEmpirical Mode Decomposition

Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

Step 1: Find the envelope alone local maximum and minimumStep 1: Find the envelope alone local maximum and minimum

Empirical Mode DecompositionEmpirical Mode Decomposition

Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

Step 2: Find the average between envelopesStep 2: Find the average between envelopes

Empirical Mode DecompositionEmpirical Mode Decomposition

Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

Step 3: To determine the fluctuation of original signal around the average of envelopes

Step 3: To determine the fluctuation of original signal around the average of envelopes

Intrinsic Mode FunctionIntrinsic Mode Function

Empirical Mode DecompositionEmpirical Mode Decomposition

Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

1 1

1 2 2

n 1 n n

n

j nj 1

x( t ) c r ,

r c r ,

x( t ) c r

. . .

r c r .

.

Sifting : to get all IMF components

0 10 20 30 40 50 60

-2

0

2O

rigin

al D

ata

0 10 20 30 40 50 60-1

0

1

IMF

1

0 10 20 30 40 50 60-1

0

1

IMF

2

0 10 20 30 40 50 60

-0.5

0

0.5

IMF

3

Empirical Mode DecompositionEmpirical Mode DecompositionA Simple ExampleA Simple Example

Empirical Mode Empirical Mode DecompositionDecomposition

Original blood pressure waveform

Key mode of blood pressure waveform during Valsalva maneuver

Blood Pressure versus Blood Flow VelocityBlood Pressure versus Blood Flow VelocityTemporal (time) RelationshipTemporal (time) Relationship

Novak V et al., Biomed Eng Online. 2004;3(1):39

Blood Pressure versus Blood Flow VelocityBlood Pressure versus Blood Flow VelocityPhase RelationshipPhase 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 IndicesConventional Autoregulation Indices

Novak V et al., Biomed Eng Online. 2004;3(1):39

Summary: Original Version of Summary: Original Version of MMPF AnalysisMMPF 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 Limitations: Original Version of MMPF Analysisof 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 Subsequent Improvements of MMPF AnalysisMMPF Analysis

Use Ensemble EMD (EEMD) Analysis

Resting-state MMPF Analysis

Selection of key mode related to respiration during resting-state condition

Comparison of phase shifts in multiple time scales

Implementation and automation of the method

K. Hu, et al., (2008) Cardiovascular Engineering

M-T Lo, k Hu et al., (2008) EURASIP Journal on Advances in Signal Processing

Wu, Z., et al. (2007) Proc. Natl. Acad. Sci. USA., 104, 14889-14894

Dr. Yanhui Liu. DynaDx Corp. U.S.A.

Hu K et al., (2012) PLoS Comput Biol 8(7): e1002601

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 Cerebral Blood Flow Regulation at Multiple Time ScalesMultiple Time Scales

Hu K et al., PLoS Comput Biol 2012; 8(7): e1002601

k. Hu, M-T Lo et al., journal of neurotrauma, 2009

Traumatic Brain Injury and Cerebral Autoregulation

Traumatic Brain Injury and Cerebral Autoregulation

k. Hu, M-T Lo et al., journal of neurotrauma, 2009

Midline Shift Correlates to Left-Right Difference in Autoregulation

k. Hu, M-T Lo et al., journal of neurotrauma, 2009

ResourcesResources 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/DataDemonSetupPRO.msi

For 32-bit system,https://dl.dropbox.com/u/7955307/daily_build/x86/DataDemonSetupPRO.msi

AcknowledgementsAcknowledgements

Vera Novak, MD, PhD Chung-Kang Peng, PhD Albert C. Yang, MD, PhD

Ment-Zung Lo, PhDKun Hu, PhDYanhui Liu, PhD