Home  | Publications | ASS+25a

Beat-to-Beat Aortic Valve Opening Detection From Impedance Cardiography Using Machine Learning

MCML Authors

Link to Profile Björn Eskofier PI Matchmaking

Björn Eskofier

Prof. Dr.

Principal Investigator

Artificial Intelligence in Medicine

Abstract

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.

misc ASS+25a

Preprint

Dec. 2025

Authors

L. Abel • S. Stühler • T. Steigleder • C. Ostgathe • N. Rohleder • B. M. Eskofier • R. Richer

Links

DOI

Research Area

 C1 | Medicine

BibTeXKey: ASS+25a

#p-eskofier
Back to Top