EE Dept., IIT Bombay NCC 2015, Mumbai, 27 th Feb. – 1 st March 2015, Paper ID #1570057501 (28 th...

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NCC 2015, Mumbai, 27th Feb. – 1st March 2015, Paper ID #1570057501 (28th Feb., Sat., Session S5, 14.00 – 15.40, Paper 2)m

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Monitoring of Stroke Volume through Impedance Cardiography Using an

Artificial Neural Network

S. M. M. Naidu, U. R. Bagal, P. C. Pandey,S. Hardas, N. D. Khambete

EE Dept., IIT Bombay

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Presentation Outline

1. Introduction

2. Equations for SV Estimation

3. ANN Based SV Estimation

4. Experimental Method

5. Results

6. Conclusion

1. Intro. | 2. Eqn for SV Esti. | 3. ANN based SV Esti. | 4. Exp. Method | 5. Results | 6. Conclusion

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1. IntroductionImpedance cardiography

A noninvasive technique based on monitoring the variation in the thoracic impedance Z(t) caused by variation in the blood volume in the thorax.

•Impedance cardiogram signal ICG = – dZ/dt

•Applications: estimation of stroke volume (SV) & some other cardio-vascular indices

•Artifacts in ICG: motion & respiratory artifacts

•Commonly used signal processing for artifacts suppression: Ensemble averaging, leading to averaging of latencies & distortions in the ICG features.


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2. SV Estimation Eqns.ICG characteristic points A point: small negative deflection before B point, associated with atrial contraction, after ECG P-wave.

B point: deflection before C point, associated with aortic valve opening, coincides with first heart sound.

C point: peak in ICG, associated with max. acceleration of blood ejected from left ventricle

X point: valley in ICG, associated with aortic valve closure, coincides with second heart sound.

O point: peak after X point, wide opening of the mitral valve. Fig. 1 ECG and other related signals, adapted from [6].

ICG(-dZ/dt)

B

C

XlvetT

15%

Ejectiontime

Measured

-Z(t)

PCG

ECG

(-dZ/dt)max

|zmax|

O

A


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SV equations

Kubicek equation (Kubicek et al.,1974): based on cylindrical parallel conductor model of thorax

SV: stroke volume (mL), : resistivity of blood ( -cm), L: distance between the voltage sensing electrodes (cm), Z0: basal impedance (), (–dZ/dt)max: maximum value of rate of change in impedance ( /s), Tlvet : left ventricular ejection time (s)

Sramek equation (Sramek et al.,1980): parallel conductor model with truncated cone

Kubicek

2SV lvet2 max

L dZT

dtZo

3max

Sramek lvet0

SV4.25

dZ dtLT

Z


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Sramek-Bernstein equation (Bernstein, 1986): truncated cone & an empirical factor

: ratio of actual-to-ideal body weight

Bernstein equation (Bernstein et al., 2005): thorax as a multi-component parallel conductor

VITBV: intra-thoracic blood volume (kg), : index of transthoracic conduction

3max

Sramek-Bernstein lvet0

SV4.25

dZ dtLT

Z

lvetmax2BernsteinSV ITBVo

VdZ dt Z T


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Investigations on applications of SV monitoring

Kim (1989): diagnosis of pulmonary artery, dilated cardiomyopathy, aortic stenosis & other valvular diseases by monitoring the SV during exercise.

Zang et al. (2008): monitoring cardiovascular function under transient conditions (supine, upright, and sitting positions).

Sherwood et al.(1998): assessing the effect of physical exercise, sleep, & use of drugs on the cardiovascular system.

Wong et al. (2009): monitoring the systolic blood pressure.

Limitations of impedance cardiography for SV estimation

Lack of high repeatability with the reference methods.

Causes: SV equations based on simplified models of thoracic impedance with several assumptions, errors in detection of the characteristic points due to distortions caused by ensemble averaging


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3. ANN Based SV EstimationANN model•Three-layer feed-forward network with L-M or gradient-decent learning algorithm: effective in tracking nonlinear input-output relationships. •May be used for SV estimation, avoiding the assumptions in the thoracic impedance model based methods.

Earlier investigations on ANN-based SV estimation

Ajitkumar et al. (1998): Three-layer feed-forward neural network, using back propagation algorithm for training. Reference: Doppler. Study: 360 cycles, 20 healthy subjects, 3 body positions (horizontal, supine head-down, & head-up), recordings during 5 s breath-hold for 6 times over 2 min. Corr. coeff.: 0.32 for Kubicek, 0.34 for Sramek, & 0.87 for ANN.


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Baker et al. (2000, US patent): Feed-forward ANN for semi-continuous calculation. Reference: Thermodilution. Network trained using back propagation algorithm. Standard error: 0.84 L/min.

Baura (1996, US patent): 3-layer feed-forward ANN. Reference: Thermodilution. Network trained using back propagation algorithm. Results not reported.

Summary of earlier investigations•Input parameters obtained from ensemble averaged ICG,•Training & testing with data across the subjects.


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Proposed technique for ANN-based SV estimation

• Beat-by-beat calculation of ICG parameters without ensemble averaging, using our method for automated detection of characteristic points (Naidu et al., 2014),

• Use of simultaneously recorded Doppler echocardiography as the reference,

• Training & testing using beat-by-beat ICG parameters, estimated from recordings with significant cardiac variability introduced by an exercise protocol,

• Evaluation using within & across the subjects datasets.


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4. Experimental MethodA. Data recording

•ICG & Doppler echocardiography recordings in clinical setup, under rest & post-exercise conditions,

•Echocardiography: LVOT blood velocity, aortic valve dia. (‘iE33 Ultrasound System’, Philips; ‘MyLab 25 Gold’, Esaote),

•Impedance cardiography: ICG, ECG, basal impedance, variation in thoracic impedance (‘HIC-2000 Bio-electric Impedance Cardiograph’, Bio-Impedance Technology), acquired using 8-ch. 16-bit DAQ (‘KUSB-3100’, Keithely), sampling freq. = 500 Hz.


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B. Subjects & exercise protocol

Subjects: six healthy male, Age: 18 – 50 years (mean = 27.3 years, SD = 12.0 years), Height: 161 – 173 cm (mean = 167.8 cm, SD = 4.2 cm), Weight: 58 – 88 kg (mean = 67.33, SD = 11.0 kg). Exercise protocol: 10 min. of exercise to introduce high heart rate variability.

C. Artifacts suppression & detection of ICG points

Denoising: wavelet-based denoising technique with scale dependent thresholding for respiratory artifact suppression (Pandey et al., 2007); subject was asked to avoid any movement to minimize motion artifact.

ICG points detection: automatic detection of ICG points using ECG-R peaks as reference (Naidu et al., 2014).


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D. SV from Doppler echocardiography SV = Velocity time integral (VTI) x Cross-sectional area (CSA)

VTI calculation •Integration of instantaneous peak velocities over the ejection period (equal to the area below the base line & the Doppler spectrum) . •Measured using built-in s/w of the machine by tracing the envelope.

CSA: from dia. at aortic annulus in PLAX view, during mid-systole, at LVOT.

Example: Doppler echo. & VTI(Upper: Apical 5-chamber viewMiddle: ECG, Lower: Doppler spectrum, LVOT velocity profile)


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E. ANN model implementation

•Three-layer feed-forward ANN; Training: back propagation with stopping criteria of RMS error & iteration count.

•Inputs: parameters from ICG, subject height & weight.

•Models with input parameters as used in the SV eqns.: NNKB (Kubicek eqn.), NNSR (Sramek eqn.), NNSB (Sramek-Bernstein eqn.), NNBR (Bernstein eqn.), NNCI (with combined inputs used in the SV eqns.)

•ICG data: parameters from original ICG & denoised ICG, beat-by-beat (without ensemble averaging).

•Training & testing: Beat-by-beat SV values from simultaneous Doppler echocardiography as reference; Training: 60% of cycles selected randomly, testing: remaining 40%. Carried out for data of cycles from individual subjects & for data of pooled cycles.


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5. Results•Evaluation: Mean & SD of errors; Corr. coeff. (for estimations using equations & ANN models).

•Summary of results: Equation based methods resulted in high mean & SD of the errors and low corr. coeff. (for data from individual subjects & for data pooled across the subjects).


Esti. Method SD-Error % (SV = 80 mL) Corr. coeff.

Kubicek eqn. 16.3 – 36.1 0.07 – 0.57

Sramek eqn. 17.5 – 37.6 0.06 – 0.50

Bernstein eqn. 12.4 – 27.9 0.08 – 0.39

NNKB1 3.0 – 7.1 0.57 – 0.77

NNBR1 3.9 – 6.1 0.55 – 0.81

NNCI2 2.5 – 10.1 0.63 – 0.87

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6. Conclusion

•ANN-based SV estimation using training with beat-by-beat data much more effective than equation based methods.

•Further investigationsoEffect of hidden layer neuron unitsoEffect of more ICG parameters & data transformationsoData from a larger number of subjects & different postures during recording.


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THANK YOU


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Abstract—Impedance cardiography is a noninvasive technique for estimation of stroke volume (SV), based on monitoring the variation in the thoracic impedance during the cardiac cycle. The current SV calculation methods use parameters obtained by ensemble averaging of the waveform along with equations based on simplified models of the thoracic impedance and aortic blood flow profile. They often result in inconsistent estimates when compared with the reference techniques. An investigation is carried out for beat-by-beat monitoring of SV using an artificial neural network with a set of input parameters as used in the different SV equations. A three-layer feed-forward neural network is used and the impedance cardiogram parameters are obtained using an algorithm for beat-by-beat automatic detection of the characteristic points. The training and testing are carried out using the SV values obtained from Doppler echocardiography as a reference technique after alignment of the signals from the two techniques. Results from the data from six subjects with recordings under rest and post-exercise conditions show the neural network based estimation to be more effective than the estimations based on SV equations.


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References

[1] A. C. Guyton, Textbook of Medical Physiology, 7th ed., Saunders, Philadelphia, 1986.

[2] W. G. Kubicek , J. Kottke, M. U. Ramos, R. P. Patterson, D. A. Witsoe, J. W. Labree, W. Remole, T. E. Layman, H. Schoening, and J. T. Garamela "The Minnesota impedance cardiograph – theory and applications," Biomed. Eng., vol. 9(9), pp. 410-416, 1974.

[3] J. Yakimets and L. Jensen, “Evaluation of impedance cardiography: comparison of NCCOM3-R7 with Fick and thermodilution methods,” Heart Lung., vol. 24(3), pp. 194-206, 1995.

[4] J. F. Lewis, L. C. Kuo, J. G. Nelson, M. C. Limacher, and M. A. Quinones, “Pulsed Doppler echocardiographic determination of stroke volume and cardiac output: clinical validation of two new methods using the apical window,” Circulation, vol. 70(3), pp. 425-431, 1984.

[5] G. E. Peterson, M. E. Brickner, and S. C. Reimold, “Transesophageal echocardiography clinical indications and applications,” Circulation, 107, pp. 2398-2402, 2003.

[6] R. P. Patterson, "Fundamentals of impedance cardiography," IEEE Eng. Med. Biol. Mag., vol. 8(1), pp. 35-38, 1989.

[7] B. E. Hurwitz, L. Y. Shyu, S. P. Reddy, N. Schneiderman, and J. H. Nagel, “Coherent ensemble averaging techniques for impedance cardiography,” in Proc. 3rd Ann. IEEE Symp. CBMS, Chapel Hill, NC, 1990, pp. 228-235.

[8] J. N. Karnegis and W. G. Kubicek, “Physiological correlates of the cardiac thoracic impedance waveform,” Am. Heart. J., vol. 79(4), pp. 519-523, 1970.

[9] Z. Lababidi, D. A. Ehmke, R. E. Durnin, P. E. Leaverton and R. M. Lauer, “The first derivative thoracic impedance cardiogram,” Circulation, vol. 41(4), pp. 651-658, 1970.

EE Dept., IIT Bombay NCC 2015, Mumbai, 27 th Feb. – 1 st March 2015, Paper ID #1570057501 (28 th...

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Transcript of EE Dept., IIT Bombay NCC 2015, Mumbai, 27 th Feb. – 1 st March 2015, Paper ID #1570057501 (28 th...