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Multimodal Pressure-Flow Analysis to Assess Dynamic Cerebral Autoregulation

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.

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Multimodal Pressure-Flow Analysis to Assess Dynamic Cerebral Autoregulation

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  1. 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 ccyang@physionet.org

  2. Overview • What is cerebral autoregulation and how to measure it? • Multimodal pressure-flow analysis • Empirical Mode Decomposition and Hilbert-Huang Transform • Subsequent improvement • Demonstration

  3. Perturbation Baseline Restored steady state 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

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

  5. Static Autoregulation Measurement Tiecks FP et al., Stroke. 1995; 26: 1014-1019

  6. Dynamic Autoregulation Measurement Tiecks FP et al., Stroke. 1995; 26: 1014-1019

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

  8. 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

  9. Multimodal Pressure-Flow Analysis

  10. 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

  11. 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

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

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

  14. 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!

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

  16. 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.

  17. Empirical Mode Decomposition Step 2: Find the average between envelopes Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

  18. Intrinsic Mode Function Empirical Mode Decomposition 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.

  19. Empirical Mode Decomposition Sifting : to get all IMF components Huang et al. Proc Roy Soc Lond A 1998;454:903-995.

  20. Empirical Mode DecompositionA Simple Example

  21. Empirical Mode Decomposition Original blood pressure waveform Key mode of blood pressure waveform during Valsalva maneuver

  22. Blood Pressure versus Blood Flow VelocityTemporal (time) Relationship Novak V et al., Biomed Eng Online. 2004;3(1):39

  23. Blood Pressure versus Blood Flow VelocityPhase Relationship Control Stroke Novak V et al., Biomed Eng Online. 2004;3(1):39

  24. Between Groups Phase Comparisons *** p < 0.005, ** p < 0.01 Groups BPR Values Comparisons +++ p <0.001

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

  26. 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.

  27. 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

  28. 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 Wu, Z., et al. (2007) Proc. Natl. Acad. Sci. USA., 104, 14889-14894 K. Hu, et al., (2008) Cardiovascular Engineering M-T Lo, k Hu et al., (2008) EURASIP Journal on Advances in Signal Processing Hu K et al., (2012) PLoS Comput Biol 8(7): e1002601 Dr. Yanhui Liu. DynaDx Corp. U.S.A.

  29. Resting-State Multimodal Pressure-Flow Analysis K. Hu, et al., Cardiovascular Engineering, 2008.

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

  31. Resting-State Multimodal Pressure-Flow Analysis

  32. Resting-State Multimodal Pressure-Flow Analysis

  33. Cerebral Blood Flow Regulation at Multiple Time Scales Hu K et al., PLoS Comput Biol 2012; 8(7): e1002601

  34. Traumatic Brain Injury and Cerebral Autoregulation k. Hu, M-T Lo et al., journal of neurotrauma, 2009

  35. Traumatic Brain Injury and Cerebral Autoregulation k. Hu, M-T Lo et al., journal of neurotrauma, 2009

  36. Midline Shift Correlates to Left-Right Difference in Autoregulation k. Hu, M-T Lo et al., journal of neurotrauma, 2009

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

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

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