Beat-to-beat aortic valve opening detection from impedance cardiography using machine learning

Accurate detection of the aortic valve opening, represented by the B-point in the impedance cardiogram (ICG), is a critical step in non-invasive hemodynamic monitoring. The B-point is essential for deriving key parameters like the pre-ejection period (PEP) and left ventricular ejection time (LVET). However, accurately identifying this fiducial point is challenging due to a high intra-individual difference in the waveform, interfering factors like body movement, and a historical lack of publicly available datasets for evaluation. Capitalizing on recently published datasets, we propose a novel machine learning-based approach for beat-to-beat B-point detection that leverages domain knowledge from 12 different established ICG-based fiducial point detection algorithms. Using these in addition to the RR-interval as the feature set, we trained different machine learning pipelines on over 11,000 cardiac cycles. The best-performing pipeline, based on a Random Forest Regressor, achieved a considerable improvement in B-point detection accuracy and robustness. Compared to the best-performing traditional algorithm, our approach reduced the mean absolute error (MAE) by 54.7 % and its standard deviation by 47.4 %, resulting in a final MAE of 8.13 ± 12.12 ms. Our novel B-point detection method will be made available for the research community by integrating it in the open-source framework PEPbench .