arXiv subjects

Andreas Körner

Publications and source records attributed to Andreas Körner.

5 recordsLinked to original sources

arXiv2026

Introducing entropy measures to PK/PD models in propofol anesthesia as a replacement of BIS

Depth of anesthesia is a complex but important vital state to analyze during a surgery or other procedure. One parameter to estimate this state is the bispectral index (BIS), a value ranging from 0 to 100 with a target of 40 to 60 for a stable state during surgery, which is based on the electroencephalogram (EEG). Despite its widespread clinical use, the BIS remains the subject of ongoing discussion as the exact algorithm underlying the BIS is not publicly disclosed, motivating the search for alternative EEG-based indices. In this publication, two entropy-based EEG measures, Permutation Entropy (PeEn) and Entropy of Difference (EoD) are investigated as potential alternatives to replace the BIS for anesthesia monitoring. Both measures quantify the complexity and irregularity of EEG signals and have previously been proposed as indicators of changes in consciousness and anesthetic depth. Their performance is evaluated by comparing with the simulated BIS values generated using the pharmacokinetic/pharmacodynamic (PK/PD) models, proposed by Marsh, Schnider and Eleveld. For each model, the root mean squared error (RMSE) between the simulated BIS and the recorded BIS, PeEn, or EoD is calculated, respectively. Statistical analysis using the Wilcoxon rank-sum test reveal no significant differences between the median RMSE values of the simulated BIS, PeEn, and EoD across the investigated PK/PD models. These results suggest that PeEn and EoD provide performance comparable to that of the simulated BIS, indicating that they may represent promising EEG-based indicators for monitoring depth of anesthesia.

q-bio.QM↗

arXiv2026

Comparison of neural network training strategies for the simulation of dynamical systems

Neural networks have become a widely adopted tool for modeling nonlinear dynamical systems from data. However, the choice of training strategy remains a key design decision, particularly for simulation tasks. This paper compares two predominant strategies: parallel and series-parallel training. The conducted empirical analysis spans five neural network architectures and two examples: a pneumatic valve test bench and an industrial robot benchmark. The study reveals that, even though series-parallel training dominates current practice, parallel training consistently yields better long-term prediction accuracy. Additionally, this work clarifies the often inconsistent terminology in the literature and relate both strategies to concepts from system identification. The findings suggest that parallel training should be considered the default training strategy for neural network-based simulation of dynamical systems.

stat.ML↗

arXiv2023

Data-based Model Identification of the Hypothalamus-Pituitary-Thyroid Complex

The thyroid gland, in conjunction with the pituitary and the hypothalamus, forms a regulated system due to their mutual influence through released hormones. The equilibrium point of this system, commonly referred to as the "set point", is individually determined. This means that determining the correct amount of medication to be administered to patients with hypothyroidism requires several treatment appointments creating an extended treatment process. Because the dynamics of the system have not yet been fully explored, mathematical models are needed to simulate the mutual influence of the respective hormones as well as their course over time. These models enable a deeper understanding of the functionality in the context of data measurements. Therefore, two existing time-dependent mathematical models are used and further analyzed to replicate this overall influence of disparate systems. Both are based on a system of two differential equations modelling the interacting hormones. The parameters of the two models are identified according to different calibration approaches by means of patient data collected in a retrospective study in collaboration with the Medical University of Vienna. The hormonal course in the time domain as well as equilibrium curves including the set-point are then simulated and analyzed with respect to the normalized mean squared error. These calibrated systems allow a more profound insight into the functionality of the formed complex.

q-bio.TO↗

arXiv2023

Permutation Entropy as a Conceptual Model to Analyse Brain Activity in Sleep

Sleep stage classification is a widely discussed topic, due to its importance in the diagnosis of sleep disorders, e.g. insomnia. Analysis of the brain activity during sleep is necessary to gain further insight into the processing that occurs in our brains. We want to use permutation entropy as a model for this analysis. Therefore, the signal processing in terms of electroencephalography is described. This results in a time discrete signal, that can be further processed by applying the method of permutation entropy, which is a modification of the Shannon entropy as a measure of information processing. The method is applied to 18 data sets, nine electroencephalography measurements of patients suffering from insomnia and nine of people without a sleep disorder. A strong correlation between the permutation entropy value and the sleep stages was found during the simulation runs. The results are analysed and presented using boxplot diagrams of the permutation entropy over the sleep stages. Furthermore, it is investigated that there is a steady decrease in the value when the patient is in a deeper sleep. This suggests that the method is a good parameter for sleep stage classification. Finally, we propose an extension of the conceptual model to other pathological conditions and also to the analysis of brain activity during surgery.

eess.SP↗

arXiv2022

Tailoring the Acidity of Liquid Media with Ionizing Radiation -- Rethinking the Acid-Base Correlation Beyond pH

Advanced in situ techniques based on electrons and X-rays are increasingly used to gain insights into fundamental materials dynamics in liquid media. Yet, ionizing radiation changes the solution chemistry. In this work, we show that ionizing radiation decouples the acidity from autoprotolysis. Consequently, pH is insufficient to capture the acidity of water-based systems under irradiation. Via radiolysis simulations, we provide a more conclusive description of the acid-base interplay. Finally, we demonstrate that acidity can be tailored by adjusting the dose rate and adding pH-irrelevant species. This opens up a huge parameter landscape for studies involving ionizing radiation.

physics.chem-ph↗