1. E MPIRICAL M ODE D ECOMPOSITION BASED T ECHNIQUE APPLIED IN EXPERIMENTAL BIOSIGNALS Alexandros Karagiannis Mobile Radio Communications Laboratory School of Electrical and Computer Engineering National Technical University of Athens

2. O UTLINE Respiration Signal Monitoring Empirical Mode Decomposition (EMD) EMD based technique proposed in this presentation Experimental procedure - Sensor Network Processing procedure Experimental Results Conclusions 2 Biosignal - Respiration Signal Standard Hospital Equipment Miniaturized sensors

3. R ESPIRATION MONITORING 3 Acceleration Vector Respiration Mechanism is comprised of changes in some physical quantities such 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

4. E MPIRICAL MODE DECOMPOSITION Method 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

5. E MPIRICAL MODE DECOMPOSITION ALGORITHM 1. Local maxima and minima of d 0 (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 e u (t) and e l (t), respectively. 3. Compute the mean of the envelopes m(t): 4. Extract the detail d 1 (t) = d 0 (t)-m(t) (sifting process) 5. Iterate steps 1-4 on the residual until the detail signal d k (t) can be considered an IMF: c 1 (t)= d k (t) 6. Iterate steps 1-5 on the residual r n (t)=x(t) - c n (t) in order to obtain all the IMFs c 1 (t),.., c N (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

6. E MPIRICAL MODE DECOMPOSITION 6 Mathematical Expression of EMD processed signal Lower order IMFs capture fast oscillation modes while higher order IMFs capture 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 setresidual

7. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE APPLIED ON BIOSIGNALS Basic Idea 1. Partial Signal Reconstruction by appropriate selection of IMFs and exclusion of those IMFs that contribute to the noise contamination and feature distortion of the signal. 2. Some IMFs of the produced IMF set may have a physical meaning while other IMFs don’t have a physical meaning. How can we select the appropriate IMFs? Application of criteria on each IMF based on the spectral characteristics (frequency,power) of each IMF corresponding to the spectral characteristics of the signal that we wish to delineate. Decision stage for IMF selection or exclusion. Determination of spectral characteristics of experimental signals 1. Literature documentation 2. Statistically identify the spectral content of the experimental signals 7

8. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS 8 Apply spectral criteria on i-th IMF EMD processed Experimental Respiratory Signal

9. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS Experimental Procedure 9 Respiration signal sampled from the mote Respiration imported for processing

10. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS 10 1.Analog 2-axis Accelerometer Experimental Setup 2.Multichannel Sampling of X, Y axes.Data are packed in one Radio message and transmitted Channel 1 Channel 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 ADC0ADC1ADC2ADC10ADC11ADC12TimestampmoteID Destination Address Source Address GroupID handler 3.Code developed in TinyOS-NesC oriented for event driven applications.

11. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNAL Processing Procedure 1. 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 metrics computation (Cross Correlation Coefficients) 11 data EMD Set of I MF Apply Spectral Criteria on the IMF set Select IMF Partial Signal Reconstruction Metric for overall performance

12. Application of EMD based technique in both X,Y axes signal from the 2-axis accelerometer. 12 E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNAL Original Y axis signal Lower order IMFs Higher order IMFs Residual signal

13. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS 13 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 in measurement point 1 and the opposite stands for measurement point 2 X,Y axes components. Experimental Results

14. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS Experimental Results 14 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)

15. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS 15 Experimental Results 1. 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

16. E MPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS 16 Conclusions 1.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 of the 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.

17. 17 Thank you Metamorphosis by M.S. Escher