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    EMPIRICAL MODE DECOMPOSITION BASED

    TECHNIQUE APPLIED IN EXPERIMENTAL

    BIOSIGNALS

    Alexandros Karagiannis

    Mobile Radio Communications Laboratory

    School of Electrical and Computer EngineeringNational Technical University of Athens

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    RESPIRATION MONITORING

    3

    Acceleration Vector

    Respiration Mechanism is comprised of

    changes in some physical quantitiessuch as :

    1. Muscular motion

    2. Volume

    3. Pressure

    4. Flow

    Muscular contraction is composed of

    1. Low frequency movement related to

    the whole contraction (0 - 5 Hz)

    2. High frequency component due to

    vibrations (2 40Hz)

    X,Y,Z components of

    acceleration vector

    Acceleration

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    EMPIRICAL MODE DECOMPOSITIONMethod for processing nonstationary signals and signals produced by nonlinear

    processes

    Decomposition of the signal into a set of Intrinsic Mode Functions (IMF) which are

    defined as

    1. Functions with equal number of extrema and zero crossings (or at most

    differed by one)

    2. Signal must have a zero-mean

    Why Empirical Mode Decomposition?

    To determine characteristic time/frequency scales for the energy

    Method that is adaptive

    Nonlinear decomposition method for time series which are generated by an

    underlying dynamical system obeying nonlinear equations

    Basic Parts of the Empirical Mode Decomposition

    1. Interpolation technique (cubic spline)

    2. Sifting process to extract and identify intrinsic modes

    3. Numerical convergence criteria (mainly to stop the iterative process of identifying

    every IMF as well as the whole set of IMFs)

    4

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    EMPIRICAL MODE DECOMPOSITION ALGORITHM

    1. Local maxima and minima of d0(t) = x(t).

    2. Interpolate between the maxima and connect them by a cubic spline curve. The

    same applies for the minima in order to obtain the upper and lower envelopes

    eu(t) and el(t), respectively.

    3. Compute the mean of the envelopes m(t):

    4. Extract the detail d1 (t) = d0(t)-m(t) (sifting process)

    5. Iterate steps 1-4 on the residual until the detail signal dk(t) can be considered an

    IMF: c1(t)= d

    k(t)

    6. Iterate steps 1-5 on the residual rn(t)=x(t) - cn(t) in order to obtain all the IMFs

    c1(t),.., cN(t) of the signal.

    The procedure terminates when the residual signal is either a constant, a

    monotonic slope, or a function with only one extrema.

    5

    ( ) ( )( )

    2

    u le t e t

    m t

    !

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    EMPIRICAL MODE DECOMPOSITION

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    Mathematical Expression of EMD processed signal

    Lower order IMFs capture fast oscillation modes while higher order IMFscapture slow oscillation modes

    Criteria used for Numerical Convergence

    1. The sifting process ends (IMF extraction) when the range of the mean

    of the envelopes m(t) is lower than 1 (0.001) of Ci (Candidate IMF)

    2. Iteration process ends when the residue r(t) is 10% or lower of the d(t)

    IMF set residual

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNALS

    8

    Apply spectral criteria on i-th IMF

    EMD processed Experimental Respiratory Signal

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    EMPIRICAL MODE DECOMPOSITION BASED

    TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS

    Experimental Procedure

    9

    Respirationsignal sampled

    from the mote

    Respirationimported for

    processing

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    EMPIRICAL MODE DECOMPOSITION BASED

    TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS

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    1. Analog 2-axis Accelerometer

    Experimental Setup

    2. Multichannel Sampling of X, Y axes.Data are packed in one

    Radio message and transmitted

    Channel 1Channel 2

    (X axis)

    Channel 3

    (Y axis)

    00 FF FF FF FF 10 00 03 00 00 05 07 07 EB 06 0B 05 1F 07 E7 05 FF AC 4B

    ADC0 ADC1 ADC2 ADC10 ADC11 ADC12 TimestampmoteIDDestinationAddress

    Source

    Address

    GroupID

    handler

    3. Code developed in TinyOS-NesC oriented for event driven

    applications.

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNAL

    Processing Procedure1. Respiration signals were monitored in X,Y axes by measuring the

    acceleration

    2. Application of the EMD on each axis signal

    3. Application of the spectral criteria on each IMF of 2-axes respiratory

    signal

    3. Evaluation of the EMD based technique was aided by metricscomputation (Cross Correlation Coefficients)

    11

    data

    EMD

    Set of

    IMF

    Apply Spectral Criteria on

    the IMF set

    SelectIMF

    Partial Signal

    Reconstruction

    Metric for overall

    performance

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    Application of EMD based technique in both X,Y axes signal from the 2-axis

    accelerometer.

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNAL

    Original Y axis signal

    Lower order IMFs

    Higher order IMFs

    Residual signal

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNALS

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    1. Decision Stage for the selection of appropriate IMFs computes the mean

    power of the N max power peaks in order to have a smoother estimate

    and more precise view of the power spectral density of each IMF

    2. Axis components (X,Y,Z) magnitude is closely related to the measurement

    point selection

    3. Y axis component is significantly higher compared to X axis component inmeasurement point 1 and the opposite stands for measurement point 2

    X,Y axes components.

    Experimental Results

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNALS

    Experimental Results

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    1. Adaptive power threshold criterion (based on the max mean power and

    minimum mean power of each IMF) produces a smaller number of IMFs

    suitable for partial signal reconstruction. Rigid power thresholds (based on

    the minimum of mean power of all IMFs) produce greater IMF set.

    2. Different frequency ranges and power thresholds result in different IMF

    sets.3. IMF sets produced by the adaptive power threshold stage suitable for

    partial signal reconstruction have smaller correlation with the original axis

    signal without compromising the characteristics of the signal. (Trade Off)

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNALS

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    Experimental Results1. High frequency denoising due to removal from the IMF set of the lower

    order IMFs is accomplished without altering the characteristic attributes

    of the signal

    2. Adaptive power threshold stage is more effective in filtering after the partial

    signal reconstruction rather than rigid power thresholds. This is due to the

    smaller IMF sets.

    Measurement

    point 2

    X axis

    Measurement

    point 2

    Y axis

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    EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE

    ALGORITHM APPLIED ON BIOSIGNALS

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    Conclusions1. Empirical Mode Decomposition based technique that utilize the decomposition of

    the signal to IMFs in order to apply a Partial Signal Reconstruction process

    2. The proposed technique tries to identify and use at the partial signal

    reconstruction stage those IMFs that may have a physical meaning.

    3. Two stage process of the technique Decision based on the spectral

    characteristics of the IMFs (frequency, power)

    4. IMFs that satisfy conditions (frequency criterion, power criterion) are considered

    for Partial Signal Reconstruction. The others are excluded.

    5. Different conditions set by the criteria produce different IMF sets for the Partial

    Signal Reconstruction

    6. Mode mixing problem does not affect significantly the decision stage because ofthe disparate scales of the IMFs of the EMD processed respiratory signals.

    7. EMD demands high computational and memory resources. A preprocessing stage

    prior to the application of the technique reduce time and resource demands

    without compromising signal quality

    8. Future work : MIT-BIH records to apply the technique, lung sounds, Weighed

    Partial Signal Reconstruction, Implementation on sensor network node level .

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    Thank you

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