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.