PEPbench—Open, Reproducible, and Systematic Benchmarking of Automated Pre‐Ejection Period Extraction Algorithms

ABSTRACT The pre‐ejection period (PEP) is a widely used cardiac parameter in psychophysiology that reflects the duration between the onset of ventricular depolarization and the opening of the aortic valve. PEP is often used as a marker of cardiac sympathetic nervous system (SNS) activity, particular...

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Vydané v:Psychophysiology Ročník 62; číslo 11; s. e70176 - n/a
Hlavní autori: Richer, Robert, Jorkowitz, Julia, Stühler, Sebastian, Abel, Luca, Kurz, Miriam, Oesten, Marie, Griesshammer, Stefan G., Albrecht, Nils C., Küderle, Arne, Ostgathe, Christoph, Kölpin, Alexander, Steigleder, Tobias, Rohleder, Nicolas, Eskofier, Bjoern M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Blackwell Publishing Ltd 01.11.2025
Predmet:
algorithm benchmarking
Algorithms
Aortic valve
Automation
Benchmarking
B‐point
Cardiography, Impedance
Datasets
Depolarization
dZ/dt
ECG
EKG
Electrocardiography - methods
Heart
Humans
ICG
open science
PEP
Psychophysiology
Q‐peak
Reproducibility of Results
Signal Processing, Computer-Assisted
Sympathetic nervous system
Sympathetic Nervous System - physiology
Q‐peak
open science
dZ/dt
algorithm benchmarking
ECG
B‐point
PEP
ICG
ISSN:0048-5772, 1469-8986, 1469-8986, 1540-5958
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Popis
Shrnutí:ABSTRACT The pre‐ejection period (PEP) is a widely used cardiac parameter in psychophysiology that reflects the duration between the onset of ventricular depolarization and the opening of the aortic valve. PEP is often used as a marker of cardiac sympathetic nervous system (SNS) activity, particularly in within‐subject comparisons under similar hemodynamic conditions. While many algorithms for automated PEP extraction from electrocardiography (ECG) and impedance cardiography (ICG) signals (more precisely, its first derivative, dZ/dt) have been proposed in literature, they have not been systematically benchmarked. This lack of standardized algorithm comparisons originates from the absence of open‐source algorithms and annotated datasets for evaluating PEP extraction algorithms. To address this issue, we introduce PEPbench, an open‐source Python package with different Q‐peak and B‐point detection algorithms from literature that can be combined to create comprehensive PEP extraction pipelines, and a standardized framework for evaluating PEP extraction algorithms. We use PEPbench to systematically compare 108 different algorithm combinations. All combinations are evaluated on two datasets with manually annotated Q‐peaks and B‐points, which we make publicly available as the first datasets with reference PEP annotations. Our results show that the algorithms can differ vastly in their performance and that B‐point detection algorithms introduce a considerable amount of error. Thus, we suggest that automated PEP extraction algorithms should be used with caution on a beat‐to‐beat level as their error rates are relatively high. This highlights the need for open and reproducible benchmarking frameworks for PEP extraction algorithms to improve the quality of research findings in the field of psychophysiology. With PEPbench, we aim to take a first step toward this goal and encourage other researchers to engage in the evaluation of PEP extraction algorithms by contributing algorithms, data, and annotations. We hope to establish a community‐driven platform, fostering innovation and collaboration in the field of psychophysiology and beyond. Impact Statement This study introduces PEPbench, the first open‐source framework for systematically benchmarking pre‐ejection period (PEP) extraction algorithms, addressing a critical gap in the field. By providing standardized datasets with annotated ECG and dZ/dt signals, and enabling the evaluation of different PEP extraction approaches, our PEPbench framework sets a foundation for reproducible, systematic comparisons in psychophysiological research. This work fosters transparency, innovation, and collaboration, advancing the accuracy and reliability of psychophysiological studies.
Bibliografia:Tobias Steigleder, Nicolas Rohleder, and Bjoern M. Eskofier contributed equally to this work.
Funding
The work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—SFB 1483—Project‐ID 442419336, EmpkinS.
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ISSN:0048-5772
1469-8986
1469-8986
1540-5958
DOI:10.1111/psyp.70176