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
psychophysiological research that reflects the duration between the
onset of ventricular depolarization and the opening of the aortic valve
and is an established marker of sympathetic nervous system activity.
While many algorithms for automated PEP extraction from
electrocardiography (ECG) and impedance cardiography (ICG) signals have
been proposed in the literature, they have not been systematically
compared against each other. This lack of standardized algorithm
comparisons is due to 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 the
literature that can be combined to form comprehensive PEP extraction
pipelines, and a standardized framework for evaluating PEP extraction
algorithms. We use PEPbench to systematically compare 90
different algorithm combinations. All combinations are evaluated on
datasets from two different studies 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 the 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 still 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 towards this goal and
encourage other researchers to engage in the evaluation of PEP
extraction algorithms by contributing algorithms, data, and annotations.
Ultimately, we hope to establish a community-driven platform, fostering
innovation and collaboration in the field of psychophysiology and
beyond.