Volume 10, Issue 3 - Proceedings of the 2024 Annual Meeting of the Austrian Society for Biomedical Engineering (ÖGBMT)

The Electro Cardio Gram is a very valuable clinical tool to access the electric function of the heart. It provides insight into the different phases of the heart beat and various kinds of disorders which may affect them. In the past decades various algorithms to detect and delineate ECG beats have been developed. The complex-lead based algorithms compute a virtual-lead by averaging the samples of an arbitrary number of channels. In this study exponential weighted moving powers based estimates for the skewness, kurtosis and signal to noise ratio are used to select the channels which are included in the resulting complex lead signal. According to these parameters the channels are labelled bad, poor, scaled and good. A weighted average is used to to compute the next sample of the complex lead from the channels labelled good and scaled. The 75 30minutes records of the ”St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database” are used to evaluate the proposed approach. The average sensitivity increases to 99.61% and to 99.63% in total compared to 99.57% and 99.59% for the standard approach. The average and overall specificity increases to 99.65% and the F1 and F-M scores to 0.9964. This shows that the estimates provide sufficient information about the quality, to successfully reduce the distortion of the complex-lead signal by artefacts and strong muscle signals while avoiding the additional time delay imposed by standard window based methods.

In addition to neurons, glial cells make up the nervous system. These glial cells are divided into macroglial cells - including astrocytes - and microglial cells. Astrocytes have several important functions in the central nervous system, such as synthesizing transmitters and releasing them, thereby activating the surrounding neurons and astrocytes. Astrocytes do not generate action potentials, but they can transmit information to neighboring cells and react to external stimuli by employing Ca2+ dynamics. Increased neuronal activity can lead to an increased extracellular level of adenosine, reflected by a change in the metabolic state. Adenosine is a naturally occurring nucleoside distributed throughout the body as a metabolic intermediary. Its action is mediated by binding to adenosine receptors, such as the G-protein coupled receptors (GPCRs) A1 adenosine receptor. The binding of adenosine to the specific receptor type activates different signaling pathways in astrocytes. In our work, we studied Ca2+ signals generated upon adenosine A1 receptor activation by adding the pathway to the computational astrocyte model by Oschmann et.al (2017). We investigated the influence of the high-affinity A1 adenosine receptor in various astrocyte domains on the regulated Ca2+ release from the endoplasmic reticulum (ER) of astrocytes and concluded that the receptor-dependent pathway contributes to Ca2+ signals mainly in the soma.

Astrocytes are specialized glial cells that cover the entire central nervous system and play an important role in synaptic transmission as well as ion and neurotransmitter homeostasis. Since the functions of astrocytes are very complex, computer models can aid experiments. We used the multi-compartment model from Oschmann et al. (2017) to analyze the effects of glutamate transporters. Those transporters are important for the removal of glutamate from the synaptic cleft as well as on the Ca2+ dynamics and the glutamate currents. Several studies have shown that the expression of these transporters changes during the course of life and that there can be a natural decline with increasing age. This may be one of the most important risk factors in neurodegenerative diseases. For this reason, we analyzed the influence of the different protein densities of the glutamate transporters EAAT1/GLAST and EAAT2/GLT-1 regarding different age groups and species, taking into account data generated in vivo and in vitro.

This study presents a finite element model (FEM) simulation of optoelectronic stimulation in the rat brain, designed to investigate the effects of this emerging neural stimulation technique. A detailed 3D model of the rat brain and an equivalent circuit representation of the organic semiconductor-based stimulation device was used to simulate the subdural application of optoelectronic stimuli to the rat brain. The model is based on realistic electrode geometries and material properties. The stimulated brain volume is approximately 38 mm3 with a maximal stimulation depth of 2.6 mm. Our results indicate that optoelectronic stimulation can achieve targeted activation of cortical neurons. Further studies will focus on optimizing device parameters, exploring long-term effects, and expand this approach for specific neural bioelectronics to fully exploit its potential in clinical applications.

Residual fragments can remain after kidney stone extraction which may necessitate another intervention. A new approach needs to be developedis needed to lower the risk of disease severe side effects caused by these fragments. Magnetic nanoparticles can be applied due to their properties of being able to bind to residual fragments and to be extracted by an external magnetic field. Fractals and magnetic nanoparticles have been synthesized and characterized thoroughly. The small nanoparticles bind covalently to the kidney stones and a magnetic extraction of calcium oxalate fractals with magnetic nanoparticles is possible. The aggregation of nanoscale iron oxide nanoparticles can be induced with the addition of salt which improves the extraction process. The separation is validated by simulation and experimentally in a porcine kidney as well as in artificial kidney models. This proof-of-concept study successfullyis the fundament for a new waydemonstrated of extracting kidney stones extraction and including their fractals and might pave the way for new procedures in urology.

Augmented reality (AR) technologies enhance a user’s physical environment by providing contextual information about their surroundings. This information might appear incongruent to users, either due to their current mental context or factual errors in the data. This paper explores the feasibility of incongruency decoding using electroencephalographic (EEG) signals from 19 participants acquired during an interactive AR task. Previous studies on single-trial N400 decoding for brain-computer interfaces using EEG data are limited. Therefore, we implemented commonly used classification approaches and assessed their decoding performance compared to the convolutional neural network EEGNet. We found that the investigated approaches offer comparable accuracies ranging from 63.3% to 64.8%. Successful decoding of incongruency effects can foster more contextually appropriate interactions within AR environments.