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

Overview  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 stateBaseline Perturbation 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. Underlying notion of “constant,” “steady-state,” conditions. Walter Cannon 1929

Ideal Cerebral Autoregulation Lassen NA. Physiol Rev. 1959;39: Strandgaard S, Paulson OB. Stroke.1984;15:

Static Autoregulation Measurement Tiecks FP et al., Stroke. 1995; 26:

Dynamic Autoregulation Measurement Tiecks FP et al., Stroke. 1995; 26:

Autoregulation Index Tiecks FP et al., Stroke. 1995; 26:

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 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

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 I. Expiration - mechanical II. reduced venous return, BP falls III. Inspiration - mechanical IV. increased cardiac output and increased peripheral resistance

Valsalva Maneuver Dynamics Blood Pressure Blood Flow Velocity – Right Middle Cerebral Artery Blood Flow Velocity – Left Middle Cerebral Artery

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,  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 Decomposition Huang et al. Proc Roy Soc Lond A 1998;454:

Empirical Mode Decomposition Huang et al. Proc Roy Soc Lond A 1998;454: Step 1: Find the envelope alone local maximum and minimum

Empirical Mode Decomposition Huang et al. Proc Roy Soc Lond A 1998;454: Step 2: Find the average between envelopes

Empirical Mode Decomposition Huang et al. Proc Roy Soc Lond A 1998;454: Step 3: To determine the fluctuation of original signal around the average of envelopes Intrinsic Mode Function

Empirical Mode Decomposition Huang et al. Proc Roy Soc Lond A 1998;454: Sifting : to get all IMF components

Empirical Mode Decomposition A Simple Example

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  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, Dr. Yanhui Liu. DynaDx Corp. U.S.A. Hu K et al., (2012) PLoS Comput Biol 8(7): e

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

Cerebral Blood Flow Regulation at Multiple Time Scales Hu K et al., PLoS Comput Biol 2012; 8(7): e

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 Left- Right Difference in Autoregulation k. Hu, M-T Lo et al., journal of neurotrauma, 2009

Resources  Empirical Mode Decomposition (Matlab)  DataDemon (Generic Analysis Platform) For 64-bit system, DemonSetupPRO.msi DemonSetupPRO.msi For 32-bit system, DemonSetupPRO.msi DemonSetupPRO.msi

Acknowledgements Vera Novak, MD, PhD Chung-Kang Peng, PhD Albert C. Yang, MD, PhD Ment-Zung Lo, PhD Kun Hu, PhD Yanhui Liu, PhD