Electrical impedance cardiography using artificial neural networks.

This study evaluates the use of artificial neural networks to estimate stroke volume from pre-processed, thoracic impedance plethysmograph signals from 20 healthy subjects. Standard back-propagation was used to train the networks, with Doppler stroke volume estimates as the desired output. The trained networks were then compared to two classical biophysical approaches. The coefficient of determination (R2 x 100%) between the biophysical approaches and the Doppler was 8.20% and 9.90%, while it was 77.38% between the best neural network and the Doppler. Among these methods, only the neural network residuals had a significant zero mean Gaussian distribution (alpha=0.05). Our results indicate that an invertible relationship may exist between thoracic bioimpedance and stroke volume, and that artificial neural networks may offer a potentially advantageous approach for estimating stroke volume from thoracic electrical impedance, both because of their ease of use and their lack of confounding assumptions.

Adaptive control is enhanced by background estimation.

The automated control of physiological variables must often contend with an unknown and time-varying background (i.e., the output level corresponding to no input). To allow for simultaneous real-time identification of background as well as the parameters of an autoregressive moving average model with exogenous inputs (ARMAX model) during adaptive control, a "floating identifier" (FI) approach was developed which may be used with most recursive identification algorithms. This method separates input and output data into low- and high-frequency components. The high-frequency components are used to identify the ARMAX model parameters and the low-frequency components to identify background. This approach was evaluated in computer simulations and animal experiments comparing an adaptive controller coupled to the FI with the same controller coupled to two other standard least squares identifiers. In the animal experiments, sodium nitroprusside was used to control mean arterial pressure of anesthetized dogs in the presence of background changes. Results showed that with the FI, the controller performed satisfactorily, while with the other identifiers, it sometimes failed. It is concluded that the FI approach is useful when applying ARMAX-based adaptive controllers to systems in which a change in background is likely.