Sebastian Stühler

Sebastian Stühler

Bachelor's Thesis

Investigation of the Pre-Ejection Period as a Marker for Sympathetic Activity during Acute Psychosocial Stress


Luca Abel (M. Sc.), Robert Richer (M. Sc.), Prof. Dr. Björn Eskofier, Prof. Dr. Nicolas Rohleder, Nils Albrecht (M. Sc.), Prof. Dr.-Ing. habil. Alexander Kölpin


12 / 2022 – 05 / 2023


The human stress response is based on two pathways: the sympathetic nervous system (SNS) for short term responses and the slower reacting Hypothalamus-pituitary-adrenal (HPA) axis, which evokes an intensified and prolonged response to stress [1]. One of the responsibilities of these two axes is the secretion of the biomarkers alpha-amylase and cortisol, which are known to be related with stress [2], [3] and therefore typically used for stress measurement. These biomarkers are typically measured by collecting saliva samples, which do not allow continuous and real-time measurement. Therefore, there is a need for novel stress markers that circumvent these limitations. One promising marker to measure stress is the Pre-Ejection-Period (PEP), which is proposed as the best noninvasive method for assessing the sympathetic control of the heart in related work [4], [5]. Although PEP is a promising stress marker it is still underexplored, due to difficulties to detect the events needed for PEP computation [6], [7]. Consequently, there is no common practice found in previous work on how to compute PEP as reliable and easy as possible.

PEP is defined as the time between the start of the ventricular depolarization and the beginning of blood ejection from the ventricle [5], [8], [9]. The gold standard approach for measuring the PEP is the parallel measurement of electrocardiogram (ECG) and impedance cardiogram (ICG). From the ECG, the start of ventricular depolarization, which corresponds to the Q-wave onset can be extracted. From the ICG, the beginning of the blood ejection from the ventricle, which corresponds to the B-point can be obtained [10]. Both, the detection of the Q-wave onset and the detection of the B-point are sensitive to noise and prone to artifacts. Hence, reliable computation of the PEP by automatic algorithms is challenging [5]. Particularly pinpointing the correct B-point is difficult, as respiration, body movement, muscle contractions or cardiovascular pathologies can influence the shape of the dZ/dt (first derivative of the cardiac impedance) signal and might lead to different signal waveforms between and even within individuals [6]. Therefore, it is still necessary to label the B-point manually, to obtain reliable PEP measurement [6], [7]. Since manual labeling is a highly time-consuming process, evaluation of PEP is still unpractical in studies with many participants [7]. Because of the difficulties in the accurate PEP measurement, several approaches have been proposed to estimate the PEP by using simplified event detection pipelines. To circumvent the detection of the Q-wave onset, some researchers used the R-peak as starting point instead [5], [11], [12]. Another approach was to use an estimate for the B-point [6], [10], [12]. Since such strong simplifications are prone to distortions and baseline shifts, the validity of PEP estimates might be reduced [10]. However, the implications of these simplifications have not been systematically assessed yet.

The goal of this bachelor’s thesis is therefore to compare different event detection methods for PEP computation with a manually labeled gold standard. To obtain the manual labeled gold standard the decision tree for visual B-point detection stated by Árbol will be used [12]. The best performing algorithm will be applied to data collected in a study as a part of the EmpkinS collaborative research center [13]. 40 participants will perform the Trier Social Stress Test (TSST) for acute psychosocial stress induction [14] and the control version (f-TSST) [15] in randomized order on two consecutive days. Among other stress markers, ECG and ICG data will be recorded during the (f-)TSST, which will be used for PEP computation using different event detection algorithms. Afterwards, the impact of these different approaches on the final PEP outcome and its potential as marker for acute psychosocial stress will be evaluated.


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