Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model - Benouar, Sara; Kedir-Talha, Malika; Seoane, Fernando (Frontiers in Physiology, 2023)
One of the crucial steps in assessing hemodynamical parameters using Impedance Cardiography (ICG) is the detection of the characteristic points in dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output for which is required the Left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time series (NARx) Feedback Neural Networks approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is been fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes out of the typical ones with a success rate ranging between 75% to 88% in the evaluated datasets. Previously without the NARX predictive model, the successful detection rate was down to 21% - 30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X Y O Z points for both datasets.