Towards a suitable time-scale representation of cardio-respiratory

AFIRM, Acquisition, Fusion of Information and netwoRk for Medicine, team .... Statistical methods for assessing agreement between two methods of clinical ...
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Outline

What is at stake ?

Test Bench

Application

Conclusion

Towards a suitable time-scale representation of cardio-respiratory signals through EMD algorithms: a simulation and validation tool

C. Franco1,2 , J. Fontecave-Jallon1 , N. Vuillerme2 and P-Y. Guméry1 (1) TIMC-IMAG UMR 5525 Laboratory, UJF Grenoble 1-CNRS PRETA, Experimental, Theoretical and Applied cardio-Respiratory Physiology, team (2) AGIM FRE 3405 Laboratory, CNRS-UJF-EPHE-UPMF AFIRM, Acquisition, Fusion of Information and netwoRk for Medicine, team Grenoble, France Wednesday, August 31st 2011

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Outline

Context & aims in physiology in data processing

The test bench Application & results : The cardio-respiratory model Cardio-respiratory interactions analysis Test of EMD algorithms performances

Perspectives

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Outline

Context & aims in physiology in data processing

The test bench Application & results : The cardio-respiratory model Cardio-respiratory interactions analysis Test of EMD algorithms performances

Perspectives

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Outline

Context & aims in physiology in data processing

The test bench Application & results : The cardio-respiratory model Cardio-respiratory interactions analysis Test of EMD algorithms performances

Perspectives

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Outline

Context & aims in physiology in data processing

The test bench Application & results : The cardio-respiratory model Cardio-respiratory interactions analysis Test of EMD algorithms performances

Perspectives

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Outline

What is at stake ?

Context

Test Bench

Application

Conclusion

& aims

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Outline

What is at stake ?

Test Bench

Application

Conclusion

From a physiological point of view

Non-invasive monitoring of the stroke volume from volumetric measurements by means of respiratory inductive plethysmography. Cardiac data retrieval from a cardio-respiratory (CR) signal.

*http://visuresp.com/index.html

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Outline

What is at stake ?

Test Bench

Application

Conclusion

From a signal processing point of view Dealing with :

non-stationnarities from respiratory and cardiac signals : swallowing, apnea, vocalization, eort... interaction between the two present physiological sources Putting back the EMD limits of separation

Figure: Behavior of EMD algorithm

towards the separation of a two-tone signal. Adapted from Rilling & Flandrin, IEEE Signal Process. Lett., 2007 [8].

Lack of analytical foundation

,→ Data-driven approach

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Outline

What is at stake ?

Test Bench

Application

Conclusion

From a signal processing point of view Dealing with :

non-stationnarities from respiratory and cardiac signals : swallowing, apnea, vocalization, eort... interaction between the two present physiological sources Putting back the EMD limits of separation

Figure: Behavior of EMD algorithm

towards the separation of a two-tone signal. Adapted from Rilling & Flandrin, IEEE Signal Process. Lett., 2007 [8].

Lack of analytical foundation

,→ Data-driven approach

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Outline

What is at stake ?

Test Bench

Application

Conclusion

From a signal processing point of view Dealing with :

non-stationnarities from respiratory and cardiac signals : swallowing, apnea, vocalization, eort... interaction between the two present physiological sources Putting back the EMD limits of separation

Figure: Behavior of EMD algorithm

towards the separation of a two-tone signal. Adapted from Rilling & Flandrin, IEEE Signal Process. Lett., 2007 [8].

Lack of analytical foundation

,→ Data-driven approach

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Outline

What is at stake ?

Test Bench

Application

Conclusion

A simulation and validation tool

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Test bench

Generation of cardiac and respiratory signals 1. Simulation

Simulated res-piratory signal Simulated cardiac signal

Simulated cardio-respira -tory signal

Time-scale representation : EMD, CEEMD algorithms 2. Data processing

Simulated stroke volume

Reconstructed respiratory signal Reconstructed cardiac signal Estimated stroke volume

SV

SV

res

ref

Bland & Altman test to compare SV with SV

Ability of EMD, CEEMD algorithms to separate components 3a. Limits of Agreement 3b. Method comparison ref

res

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Outline

What is at stake ?

Test Bench

Application

Conclusion

Application

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Outline

What is at stake ?

Test Bench

Application

Conclusion

1. Generation of simulated CR signals (Fontecave-Jallon et al., Phil. Trans. R. Soc. A, 2009) [4]

Cardiac compartment Heart volume

Vh (t ) = Ah (t ).vh (t , fh ) 1/fh

Rib cage

Amplitude modulation

A (t )

Mechanical

h

||

a (t ). h

coupling

1

c

A (t ).c2

v

Cardio-respiratory volume

Vth (t )

1/fA

Alveolar volume

VA (t ) = aA (t ).vA (t , fA )

Ventilatory compartment 8 / 22

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What is at stake ?

Test Bench

Application

Conclusion

2. Signal Processing Time-scale representations of the simulated CR signal

Figure: EMD Decomposition

Figure: CEEMD Decomposition 9 / 22

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What is at stake ?

Test Bench

Application

Conclusion

2. Signal Processing Time-scale representations of the simulated CR signal

Figure: EMD Decomposition

Figure: CEEMD Decomposition 9 / 22

Outline

What is at stake ?

Test Bench

2. CEEMD algorithm

Application

Conclusion

(Yeh et al. AADA, 2010) [9]

Signal to decompose

White noise 1

+

White noise 2

−

+

White noise n

...

−

−

+

+

−

Empirical Mode Decomposition

IMF1,1 IMF1,2

IMF2,1 IMF2,2

IMF3,1 IMF3,2

IMF4,1 IMF4,2

IMF...,1 IMF...,2

IMF...,1 IMF...,2

IMF1,m

IMF2,m

IMF3,m

IMF4,m

IMF...,m

IMF...,m

...

...

Means through the dierent scales

...

...

...

P2n

IMF1 = (

IMF = ( m

n−1,1 IMF2n,1 n−1,2 IMF2n,2 ... ...

IMF2 IMF2

n−1,m IMF2n,m

IMF2

1 IMFi ,1 )/(2n)

i=

P2n

IMF2 = (

...

=1 IMF ,2 )/(2n) ... i

P2n

i=

i

1 IMFi ,m )/(2n)

Aim : Overcoming the Mode-Mixing phenomenon (Abdulhay IEEE EMBC, 2009) [1]

et al.

Means : Redistributing the scales uniformly 10 / 22

Outline

What is at stake ?

Test Bench

Application

Conclusion

3a. Limits of agreement Cardiac volume reconstruction

Figure: Comparison of the cardiac volume (plain) reconstructed from EMD and CEEMD resp. with the one of reference (dotted) Discrepancies appear with the EMD approach 11 / 22

Outline

What is at stake ?

Test Bench

Application

Conclusion

3a. Limits of agreement Stroke volume reconstruction

Stroke volume : volume of blood ejected with each beat Amplitude of the cardiac volume at each cardiac cycle

,→

Stroke volume agreement

Bland & Altman statistical test : 92 measures over 120s Limits of agreement with 95% condence interval : up to ±30% (Critchley & Critchley, J Clin. Monitor. Comp., 1999) [3]

Figure: Bland-Altman plot (Bland &

Altman, Lancet, 1986) [2] : comparison of SV with SV estimated from CEMMD algorithm SV . ref

CEEMD

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Outline

What is at stake ?

Test Bench

Application

Conclusion

3b. Method comparison

Bland & Altman statistical test for 143 simulations Limits of agreement depending on amplitude a = aA /ah and frequency f = fA /fh ratios :

Better achievement of CEEMD approach in the separation of the cardiac and the respiratory components. 13 / 22

Outline

What is at stake ?

Test Bench

Application

Conclusion

Final thoughts Limitations

Simulation : Improvement of both the respiratory and cardiac models Signal processing : Unsupervised selection of the cardiac IMFs Future prospects

Clarify the result discrepancies observed between EMD and CEEMD algorithms (resolution, ...) (Fontecave-Jallon et al., GRETSI, 2011) [5] Validation on real data by coupling plethysmography and cardiac impedancemetry (Fontecave-Jallon et al., P'Health, 2011) [6]

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Application

Acknowledgement

This research was granted by the company IDS SA (Montceau les Mines, France) through a CIFRE process ran by ANRT and initiated by the French Ministry of Higher Education and Research.

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Application

References I [1]

E. Abdulhay, PY Gumery, J. Fontecave, and P. Baconnier.

[2]

J. M. Bland and D. G. Altman.

[3]

L. A. H. Critchley and J. A. J. H. Critchley.

[4]

J. Fontecave Jallon, E. Abdulhay, P. Calabrese, P. Baconnier, and PY. Gumery.

Cardiogenic oscillations extraction in inductive plethysmography : Ensemble empirical mode decomposition. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, volume 1, pages 22402243. Conf Proc IEEE Eng Med Biol Soc, Sept. 2009. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476) :307310, Feb 1986. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. Journal of Clinical Monitoring and Computing, 15(2) :8591, February 1999. A model of mechanical interactions between heart and lungs.

Philosophical Transactions of the Royal Society A : Mathematical,Physical and Engineering

, 367(1908) :47414757, 2009.

Sciences

[5]

J. Fontecave-Jallon, E. Abdulhay, and PY. Guméry.

[6]

J. Fontecave-Jallon, PY. Guméry, P. Calabrese, R. Briot, and P. Baconnier.

Empirical mode decomposition for the investigation of cardio-respiratory interactions within volumetric signals : a simulated approach. In GRETSI'11, 2011. A wearable technology revisited for cardio-respiratory functional exploration : Stroke volume estimation from respiratory inductive plethysmography. In pHealth, Lyon, France, July 2011 2011.

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Application

References II

[7]

G. Rilling and P. Flandrin.

[8]

G. Rilling, P. Flandrin, P. Gonalves, and J.M. Lilly.

[9]

J.R. Yeh, J.s. Shieh, and N.e. Huang.

One or two frequencies ? the empirical mode decomposition answers. Signal Processing, IEEE Transactions on, 56(1) :8595, Jan. 2008. Bivariate empirical mode decomposition. Signal Processing Letters, IEEE, 14(12) :936939, Dec. 2007. Complementary ensemble empirical mode decomposition : a novel noise enhanced data analysis method. Advances in Adaptative Data Analysis, 2(2) :135156, 2010.

Contact :

[email protected]

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Application

EMD ability to separate two nonlinear components

Figure: One or two frequencies ? Rilling's work Conclusion

EMD achievement depends on amplitude and frequency ratios from the present components[7]. 18 / 22

Application

Application to real data

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Application

Application to real data

IMF3 to IMF6 were selected and summed up to reconstruct the cardiac volume. 20 / 22

Application

Stroke volume estimation

Figure

: Comparison of the cardiac signal reconstructed from RIP with the one from ECG

Figure : For each cardiac cycle, stroke volume is estimated as the amplitude of the cardiac signal from RIP

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Application

Results for 24 healthy subjects [6] RIP versus cardiac impedance

Figure

: Correlation between stroke volumes extracted from cardiac RIP and impedance signals

Figure

: Bland & Altman plot to compare RIP and impedance measurements

Limitations :Scale mixing persist

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