Ulla Sternemann

Ulla Sternemann

Master's Thesis

Extraction of Pre-Ejection Period as Marker for Acute Psychosocial Stress from Wearable Sensors and Interferometry Radar


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

09 / 2022 – 03 / 2023


Stress is one of the leading causes of a variety of chronic diseases [1], [2]. The human body reacts to stress by triggering specific neuroendocrine responses which are mainly modulated by two main stress response pathways: the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenocortical (HPA) axis [3]. While the HPA axis is responsible for the secretion of cortisol [4], the SNS initiates the immediate ‘fight or flight’ reaction via release of epinephrine, leading to increased heart rate (HR) and cardiac contraction force, as well as the secretion of salivary alpha-amylase (sAA).

Another promising marker for sympathetic activation and, thus, the assessment of stress reactions, is the pre-ejection period (PEP) which is defined as the time of isovolumetric contraction of the left ventricle and can be considered as the delay between the electrical (initiation of contraction) and the mechanical activity (ejection of blood) of the heart [5]. PEP is primarily modulated by SNS activity and shortens with increased sympathetic activation [5], [6]. Recent research has investigated the relationship between stress and sympathetic activation and changes in PEP where sympathetic activation was typically induced via medication [6] or laboratory stress protocols [7], [8]. The gold standard of PEP measurement in the laboratory is the simultaneous recording of the electrocardiogram (ECG) and impedance cardiogram (ICG) [5].

Unfortunately, PEP assessment, as well as measuring other neuroendocrine and electrophysiological reactions of the human body, is rather effortful since it usually requires attaching electrodes to the body [9] or collecting several saliva samples to measure cortisol and sAA [3]. Thus, it interferes at least to some extent with natural human behavior [10]. For that reason, it would be very beneficial to measure stress in a less obtrusive, and possibly even contactless way. Unobtrusive PEP assessment methods can be provided by using wearable sensors, such as inertial measurement units (IMUs), to extract the seismocardiogram (SCG) from chest micro-movements [11], [12]. An even contactless way for PEP estimation might be given by using radar-based measurement of cardiac parameters. Will et al. presented a six-port interferometer to record micro-vibrations of the chest and extract heart sounds from radar data [13]. They extracted S1 heart sounds, corresponding to the closing of the atrioventricular valves just before the onset of ventricular contraction, to estimate HR and heart rate variability (HRV) [12]. Hence, it is expected that PEP estimation, similar to IMU- or radar-based measurement of cardiac parameters like HR(V), is also possible with more unobtrusive measurement methods.

The goal of this master’s thesis is, therefore, to investigate the feasibility of PEP estimation using a contactless, radar-based approach and compare it to gold standard methods as well as to established PEP estimation methods using wearable sensors. To achieve this aim, a study with n=40 participants will be conducted. During the experiment, participants will be equipped with wearable sensors located at chest and sternum to record acceleration, angular velocity, and ECG data. Additionally, an interferometry radar will be directed at the chest to measure micro-movements. As ground truth, ECG, ICG, and respiration data will be recorded using a BIOPAC MP160 system [14].

[1] D. B. O’Connor, J. F. Thayer, and K. Vedhara, “Stress and Health: A Review of Psychobiological Processes,” Annu. Rev. Psychol., vol. 72, no. 1, pp. 663–688, 2021, doi: 10.1146/annurev-psych-062520-122331.
[2] APA, “Stress in America 2019,” Am. Psychol. Assoc., 2019.
[3] I. Labuschagne, C. Grace, P. Rendell, G. Terrett and M. Heinrichs, “An introductory guide to conducting the Trier Social Stress Test,” Neurosci. Biobehav. Rev., vol. 107, pp. 686-695, 2019, doi: 10.1016/j.neubiorev.2019.09.032.
[4] Y. M. Ulrich-Lai and J. P. Herman, “Neural regulation of endocrine and autonomic stress responses,” Nat. Rev. Neurosci., vol. 10, no. 6, pp. 397–409, Jun. 2009, doi: 10.1038/nrn2647.
[5] D. B. Newlin and R. W. Levenson, “Pre-ejection Period: Measuring Beta-adrenergic Influences Upon the Heart,” Psychophysiology, vol. 16, no. 6, pp. 546-552, 1979, doi: 10.1111/j.1469-8986.1979.tb01519.x
[6] L. Drost, J. B. Finke, J. Port and H. Schächinger, “Comparison of TWA and PEP as indices of α2- and ß-adrenergic activation,” Psychopharmacology, vol. 239, pp. 2277–2288, 2022, doi: 10.1007/s00213-022-06114-8.
[7] S. Rahman, M. Habel, R. J. Contrada, “Poincaré plot indices as measures of sympathetic cardiac regulation: Responses to psychological stress and associations with pre-ejection period,” Int. J. Psychophysiology, vol. 133, pp. 79-90, 2018, doi: 10.1016/j.ijpsycho.2018.08.005.
[8] J. R. Stroop, “Studies of interference in serial verbal reactions,” J. Exp. Psychol., vol. 18, no. 6, pp. 643–662, 1935, doi: 10.1037/h0054651
[9] A. Arza, J. M. Garzón, A. Hemando, J. Aguiló and R. Bailon, “Towards an objective measurement of emotional stress: Preliminary analysis based on heart rate variability,” 37th Annu. Int. Conf. IEEE EMBC, pp. 3331-3334, 2015, doi: 10.1109/EMBC.2015.7319105.
[10] A. E. Kazdin, “Unobtrusive measures in behavioral assessment,” J. Appl. Behav. Anal., vol. 12, no. 4, pp. 713–724, 1979, doi: 10.1901/jaba.1979.12-713.
[11] M. M. H. Shandhi, B. Semiz, S. Hersek, N. Goller, F. Ayazi and O. T. Inan, “Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation,” IEEE J. Biomed. Health Inform., vol. 23, no. 6, pp. 2365-2374, 2019, doi: 10.1109/JBHI.2019.2895775.
[12] K. Shi, T. Steigleder, S. Schellenberger, F. Michler, A. Malessa, F. Lurz, N. Rohleder, C. Ostgathe, R. Weigel, A. Koelpin, “Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks,”, Sci. Rep., vol. 11, 2021, doi: 10.1038/s41598-021-81101-1.
[13] C. Will, K. Shi, S. Schellenberger, T. Steigleder, F. Michler, J. Fuchs, R. Weigel, C. Ostgathe, A. Koelpin, “Radar-Based Heart Sound Detection“, Sci. Rep., vol. 8, 2018, doi: 10.1038/s41598-018-29984-5. N13
[14] https://www.biopac.com/product-category/research/systems/mp150-starter-systems/