Benouar Sara, Kedir-Talha Malika, Seoane Fernando
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
Laboratory of Instrumentation, Department of Instrumentation and Automatics, Institute of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria.
Front Physiol. 2023 Jun 6;14:1181745. doi: 10.3389/fphys.2023.1181745. eCollection 2023.
One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the 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 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 non-linear autoregressive model with exogenous inputs (NARX) feedback neural network 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 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 with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%-30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.
使用阻抗心动图（ICG）评估血流动力学参数的关键步骤之一是检测ICG复合波中dZ/dt的特征点，尤其是X点。从ICG复合波中最常估计的参数是每搏输出量和心输出量，为此需要左心室射血前期时间。不幸的是，对于逐搏计算，检测的准确性受ICG复合波亚型变异性的影响。因此，在这项工作中，我们旨在创建一个预测模型，该模型可以预测缺失点并降低先前的缺失点工作百分比，以支持根据几种现有亚型检测ICG特征点和提取血流动力学参数。因此，实施了一种带有外部输入的时间序列非线性自回归模型（NARX）反馈神经网络方法，以根据不同的现有亚型预测缺失的ICG点。NARX在两个不同的数据集上以开环模式进行训练，以确保网络被馈入正确的反馈输入。一旦训练令人满意，就可以关闭环路进行多步预测测试和模拟。结果表明，在评估的数据集中，我们可以成功预测所有复合波中的缺失特征点，成功率在75%至88%之间。以前，在没有NARX预测模型的情况下，相同数据集的成功检测率为21%-30%。因此，这项工作表明了一种有前景的方法，并且提高了两个数据集对X、Y、O和Z点的检测准确性。
