Monitoring of Stroke Volume through Impedance Cardiography Using an Artificial Neural Network

1. 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

2. 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

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

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

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

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

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

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

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

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

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

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

13. 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 view Middle: ECG, Lower: Doppler spectrum, LVOT velocity profile) 1. Intro. | 2. Eqn for SV Esti. | 3. ANN based SV Esti. | 4. Exp. Method | 5. Results | 6. Conclusion

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

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

16. 6. Conclusion • ANN-based SV estimation using training with beat-by-beat data much more effective than equation based methods. • Further investigations • Effect of hidden layer neuron units • Effect of more ICG parameters & data transformations • Data from a larger number of subjects &different postures during recording. 1. Intro. | 2. Eqn for SV Esti. | 3. ANN based SV Esti. | 4. Exp. Method | 5. Results | 6. Conclusion

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

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

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