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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
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
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
Respiratory Signals From Blood Pressure Time Series
M-T Lo, k Hu et al., EURASIP Journal on Advances in Signal Processing, 2008
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
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