An Adaptive Data Analysis Method with Some Biomedical Applications

Norden E. HuangResearch Center for Adaptive Data Analysis

National Central University

2008

Data and Data Analysis

Data are the our only connection to the reality.

We are overwhelmed by the quantity of the data,

But underserved by the quality of the analysis.

Data Processing and Data Analysis

• Processing [proces < L. Processus < pp of Procedere = Proceed: pro- forward + cedere, to go] : A particular method of doing something.

• Data Processing >>>> Mathematically meaningful parameters

• Analysis [Gr. ana, up, throughout + lysis, a loosing] : A separating of any whole into its parts, especially with an examination of the parts to find out their nature, proportion, function, interrelationship etc.

• Data Analysis >>>> Physical understandings

Henri Poincaré

Science is built up of facts*,

as a house is built of stones;

but an accumulation of facts is no more a science

than a heap of stones is a house.

* Unexamined facts (data) are useless!

Scientific Activities

Collecting, analyzing, synthesizing, and theorizing are the core of scientific activities.

Data are our only connection to reality; they are also what separate science from philosophy.

Therefore, data analysis is a key link in this continuous loop.

Data Analysis

Data analysis is too important to be left to the mathematicians.

Why?!

Different Paradigms IMathematics vs. Science/Engineering

• Mathematicians

• Absolute proofs

• Logic consistency

• Mathematical rigor

• Scientists/Engineers

• Agreement with observations

• Physical meaning

• Working Approximations

Different Paradigms IIMathematics vs. Science/Engineering

• Mathematicians

• Idealized Spaces

• Perfect world in which everything is known

• Inconsistency in the different spaces and the real world

• Scientists/Engineers

• Real Space

• Real world in which knowledge is incomplete and limited

• Constancy in the real world within allowable approximation

Rigor vs. Reality

As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.

Albert Einstein

Traditional Data Analysis

In pursue of mathematic rigor and certainty, however, we are forced to idealize, but also deviate from, the reality.

As a result, we are forced to live in a pseudo-real world, in which all processes are

Linear and Stationary

削足適履

Trimming the foot to fit the shoe.

Available ‘Data Analysis’ Methodsfor Nonstationary (but Linear) time series

• Spectrogram• Wavelet Analysis• Wigner-Ville Distributions• Empirical Orthogonal Functions aka Singular Spectral

Analysis• Moving means• Successive differentiations

Available ‘Data Analysis’ Methodsfor Nonlinear (but Stationary and Deterministic)

time series

• Phase space method• Delay reconstruction and embedding• Poincaré surface of section• Self-similarity, attractor geometry & fractals

• Nonlinear Prediction

• Lyapunov Exponents for stability

Typical Apologia

• Assuming the process is stationary ….

• Assuming the process is locally stationary ….

• As the nonlinearity is weak, we can use perturbation approach ….

Though we can assume all we want, but

the reality cannot be bent by the assumptions.

掩耳盜鈴

Stealing the bell with muffed ears

Motivations for alternatives: Problems for Traditional Methods

• Physical processes are mostly nonstationary

• Physical Processes are mostly nonlinear

• Data from observations are invariably too short

• Physical processes are mostly non-repeatable.

Ensemble mean impossible, and temporal mean might not be meaningful for lack of stationarity and ergodicity. Traditional methods are inadequate.

The job of a scientist is to listen carefully to nature, not to tell nature how to behave.

Richard Feynman

To listen is to use adaptive method and let the data sing, and not to force the data to fit preconceived modes.

The Job of a Scientist

Characteristics of Data from Nonlinear Processes

32

2

2

22

d xx cos t

dt

d xx cos t

dt

Spring with positiondependent cons tan t ,

int ra wave frequency mod ulation;

therefore ,we need ins tan

x

1

taneous frequenc

x

y.

Duffing Pendulum

2

22( co .) s1

d xx tx

dt

x

Duffing Equation : Data

p

2 2 1 / 2 1

i ( t )

For any x( t ) L ,

1 x( )y( t ) d ,

t

then, x( t )and y( t ) form the analytic pairs:

z( t ) x( t ) i y( t ) ,

where

y( t )a( t ) x y and ( t ) tan .

x( t )

a( t ) e

Hilbert Transform : Definition

Hilbert Transform Fit

The Traditional View of the Hilbert Transform

for Data Analysis

Traditional Viewa la Hahn (1995) : Data LOD

Traditional Viewa la Hahn (1995) : Hilbert

Why the traditional approach does not work?

Hilbert Transform a cos + b : Data

Hilbert Transform a cos + b : Phase Diagram

Hilbert Transform a cos + b : Phase Angle Details

Hilbert Transform a cos + b : Frequency

The Empirical Mode Decomposition Method and

Hilbert Spectral Analysis

Sifting

Empirical Mode Decomposition: Methodology : Test Data

Empirical Mode Decomposition: Methodology : data and m1

Empirical Mode Decomposition: Methodology : data & h1

Empirical Mode Decomposition: Methodology : h1 & m2

Empirical Mode Decomposition: Methodology : h3 & m4

Empirical Mode Decomposition: Methodology : h4 & m5

Empirical Mode DecompositionSifting : to get one IMF component

1 1

1 2 2

k 1 k k

k 1

x( t ) m h ,

h m h ,

.....

.....

h m h

.h c

.

The Stoppage Criteria

The Cauchy type criterion:

when SD is small than a pre-set value, where

T2

k 1 kt 0

T2

k 1t 0

h ( t ) h ( t )SD

h ( t )

Empirical Mode Decomposition: Methodology : IMF c1

Definition of the Intrinsic Mode Function (IMF)

Any function having the same numbers of

zero cros sin gs and extrema,and also having

symmetric envelopes defined by local max ima

and min ima respectively is defined as an

Intrinsic Mode Function( IMF ).

All IMF enjoys good Hilbert Transfo

i ( t )

rm :

c( t ) a( t )e

Empirical Mode Decomposition: Methodology : data, r1 and m1

Empirical Mode DecompositionSifting : to get all the IMF components

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 .

.

Definition of Instantaneous Frequency

i ( t )

t

The Fourier Transform of the Instrinsic Mode

Funnction, c( t ), gives

W ( ) a( t ) e dt

By Stationary phase approximation we have

d ( t ),

dt

This is defined as the Ins tan taneous Frequency .

Definition of Frequency

Given the period of a wave as T ; the frequency is defined as

1.

T

Instantaneous Frequency

distanceVelocity ; mean velocity

time

dxNewton v

dt

1Frequency ; mean frequency

period

dHH

So that both v and

T defines the p

can appear in differential equations.

hase functiondt

The combination of Hilbert Spectral Analysis and

Empirical Mode Decomposition is designated as

HHT

(HHT vs. FFT)

Jean-Baptiste-Joseph Fourier

1807 “On the Propagation of Heat in Solid Bodies”

1812 Grand Prize of Paris Institute

“Théorie analytique de la chaleur”

‘... the manner in which the author arrives at these equations is not exempt of difficulties and that his analysis to integrate them still leaves something to be desired on the score of generality and even rigor.’

1817 Elected to Académie des Sciences

1822 Appointed as Secretary of Math Section

paper published

Fourier’s work is a great mathematical poem. Lord Kelvin

Comparison between FFT and HHT

j

j

t

i t

jj

i ( )d

jj

1. FFT :

x( t ) a e .

2. HHT :

x( t ) a ( t ) e .

Comparisons: Fourier, Hilbert & Wavelet

Speech Analysis Hello : Data

Four comparsions D

An Example of Sifting

Length Of Day Data

LOD : IMF

Orthogonality Check

• Pair-wise % • 0.0003• 0.0001• 0.0215• 0.0117• 0.0022• 0.0031• 0.0026• 0.0083• 0.0042• 0.0369• 0.0400

• Overall %

• 0.0452

LOD : Data & c12

LOD : Data & Sum c11-12

LOD : Data & sum c10-12

LOD : Data & c9 - 12

LOD : Data & c8 - 12

LOD : Detailed Data and Sum c8-c12

LOD : Data & c7 - 12

LOD : Detail Data and Sum IMF c7-c12

LOD : Difference Data – sum all IMFs

Traditional Viewa la Hahn (1995) : Hilbert

Mean Annual Cycle & Envelope: 9 CEI Cases

Properties of EMD Basis

The Adaptive Basis based on and derived from the data by the empirical method satisfy nearly all the traditional requirements for basis

a posteriori:

Complete

Convergent

Orthogonal

Unique

Hilbert’s View on Nonlinear Data

Duffing Equation

23

2.

Solved with for t 0 to 200 with

1

0.1

od

0.04 Hz

Initial condition :

[ x( o ) ,

d xx x c

x'( 0 ) ] [1

os t

, 1]

3

t

e2

d

tb

Duffing Equation : Data

Duffing Equation : IMFs

Duffing Equation : Hilbert Spectrum

Duffing Equation : Detailed Hilbert Spectrum

Duffing Equation : Wavelet Spectrum

Duffing Equation : Hilbert & Wavelet Spectra

What This Means

• Instantaneous Frequency offers a total different view for nonlinear data: instantaneous frequency with no need for harmonics and unlimited by uncertainty.

• Adaptive basis is indispensable for nonstationary and nonlinear data analysis

• HHT establishes a new paradigm of data analysis

Comparisons

Fourier Wavelet Hilbert

Basis a priori a priori Adaptive

Frequency Integral Transform: Global

Integral Transform: Regional

Differentiation:

Local

Presentation Energy-frequency Energy-time-frequency

Energy-time-frequency

Nonlinear no no yes

Non-stationary no yes yes

Uncertainty yes yes no

Harmonics yes yes no

Application of Hilbert Huang Transform in Cerebral Blood

Flow Regulation

Multimodal pressure-flow method to assess dynamics of cerebral auto-regulation

By Dr. Lo Meng-Yzung of RCADA, NCU and the Harvard Medical School, Division of Inter-disciplinary Medicine.

Valsalva Maneuver Dynamics

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 Dynamics

Blood Pressure

Blood Flow Velocity – Right Middle Cerebral Artery

Blood Flow Velocity – Left Middle Cerebral Artery

Empirical Mode Decomposition

Original blood pressure waveform

Key mode of blood pressure waveform during Valsalva maneuver

Blood pressure versus blood flow velocityTemporal (time) Relationship

Blood pressure versus blood flow velocityPhase Relationship

Control Stroke

EEMD can easily identify blood pressure oscillations induced by the spontaneous breathing (0.4~0.15 Hz , i.e. period 2.5~7 sec)

Cerebral Auto-regulation in stroke patients

86

Hu K, Peng CK, Lo MT, Zhao P, LaRose S, Novak P, Selim M, Lipsitz LA, Novak V (2008)

Assessment of Cerebral Autoregulation from Spontaneous Blood Pressure and Cerebral Blood Flow Fluctuations.

Stroke (abstract, accepted for publication)

Cerebral Auto-regulation in diabetes

87

Hu K, Peng CK, Huang NE, et al. (2008)

Altered phase interactions between spontaneous blood pressure and flow fluctuations in Type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation. Physica A 387 2279–2292.

Compare with Transfer Function Analysis (Fourier based method) for Diabetes with Valsalva maneuver.

0 50 100 150 200 250 300 350 400 450-2

-1

0

1

2simulated BP

0 50 100 150 200 250 300 350 400 450-2

-1

0

1

2simulated BFV

Identify the epoch with unwanted interference

0 50 100 150 200 250 300 350 400 450-4

-2

0

2

4phase difference

0 50 100 150 200 250 300 350 400 450-1

0

1

2annotation

Nonlinear pressure-flow relationship is able to detect asymmetry of brain blood circulation associated with midline shift

Dr. Marek Czosnyka, UK neuroscience

Association between the midline shift and left-right difference in BP-BFV phase shift

The difference in BP-BFV phase shifts between the right and left hemispheres was positively correlated to midline shift of the brain

Image Analysis

We have extended HHT to multi-dimensional data; A patent is being filed.

Image analysis by bidimensional empirical mode decompositionJ.C. Nunes*, Y. Bouaoune, E. Delechelle, O. Niang, Ph. Bunel

Image and Vision Computing, 2003

Conclusion

Adaptive method is the only scientifically meaningful way to analyze data.

It is the only way to find out the underlying physical processes; therefore, it is indispensable in scientific research.

It is physical, direct, and simple.

But, we have only started and what we have done is only a scratch of the surface.

Many of the most significant and interesting challenges of the modern world require boundary-crossing collaborations among scientists and scholars with widely different

fields of expertise.

Allison Richard

Vice Chancellor, Cambridge University

Current Applications

• Non-destructive Evaluation for Structural Health Monitoring

– (DOT, NSWC, and DFRC/NASA, KSC/NASA Shuttle)

• Vibration, speech, and acoustic signal analyses

– (FBI, MIT, and DARPA)

• Earthquake Engineering

– (DOT)

• Bio-medical applications

– (Harvard, UCSD, Johns Hopkins)

• Global Primary Productivity Evolution map from LandSat data

– (NASA Goddard, NOAA)

• Cosmological Gravity Wave

– (NASA Goddard)

• Financial market data analysis

– (NCU)

History of HHT1998: The Empirical Mode Decomposition Method and the Hilbert Spectrum for No

n-stationary Time Series Analysis, Proc. Roy. Soc. London, A454, 903-995. The invention of the basic method of EMD, and Hilbert transform for determining the Instantaneous Frequency and energy.

1999: A New View of Nonlinear Water Waves – The Hilbert Spectrum, Ann. Rev. Fluid Mech. 31, 417-457.

Introduction of the intermittence in decomposition. 2003: A confidence Limit for the Empirical mode decomposition and the Hilbert spe

ctral analysis, Proc. of Roy. Soc. London, A459, 2317-2345.Establishment of a confidence limit without the ergodic assumption.

2004: A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method, Proc. Roy. Soc. London, A460, 1597-1611Defined statistical significance and predictability.

2007: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci., 104, 14,889-14,894.The correct adaptive trend determination method

2009: On Ensemble Empirical Mode Decomposition. Advances in Adaptive Data Analysis 1, 1-41.

2009: On instantaneous Frequency. Advances in Adaptive Data Analysis (Accepted)

Advances in Adaptive data Analysis: Theory and Applications

A new journal to be published by the World Scientific

Under the joint Co-Editor-in-Chief

Norden E. Huang, RCADA NCUThomas Yizhao Hou, CALTECH

To be launched in the March 2008