Philipp Rostalski’s research while affiliated with University of Lübeck and other places

Objective: Respiratory surface electromyography (sEMG) is a promising physiological signal for analyzing respiratory effort, patient-ventilator asynchrony, and respiratory training. In clinical research, a wide variety of different setups are used and no consensus has yet been reached on the positioning of electrodes. Therefore, this work aims to quantitatively compare both unilateral and bilateral bipolar electrode leads. Methods: Recordings of diaphragmatic and intercostal muscle activity were performed in 20 young and healthy adults using a setup with 64 electrodes placed in relation to prominent anatomical lines. Subjects completed three breathing maneuvers: 300 s quiet breathing, 5 maximum inspiratory pressure (MIP) trials, and 15 breaths of resistance breathing at 20 % of the MIP. To quantify the performance of differential electrode leads, three metrics were determined: the ratio between inspiratory muscle activity and (1) baseline noise (SNR_base), (2) expiratory muscle activity (SNR_exp), and (3) ECG interference (SNR_EMG-ECG). Results: The study revealed considerable differences between bipolar electrode positions. Our results support the use of bilateral positions on the midclavicular line and parasternal line for measuring diaphragm and intercostal activity. For intercostal muscles, there is a high flexibility in positioning electrodes more lateral or medial, if necessary. Unilateral leads do not appear to outperform the bilateral configuration as SNR metrics were consistently smaller. Conclusion: This study provides recommendations for electrode placements and is a first step towards standardization of respiratory sEMG measurements. Significance: This electrode lead standardization will be essential to increase clinical acceptance in the future.

This paper presents a prototype for an aerial mobile network relay in the setting of pre-hospital emergency care. The requirements for such an unmanned aerial vehicle (UAV) are summarized and used to develop a hardware prototype. Gaussian processes and Bayesian optimization are implemented to find an optimal location for the aerial relay. The aerial relay enhances the network throughput by a factor of up to 7.2 in comparison to a relay on the ground. The findings show that UAV-based relays have the potential to play a vital role in emergency rescue.

Mathematical patient models play a profound role in the managing and understanding of anaesthesia. This article proposes a patient model for simulating anaesthetic uptake, i.e. the storing of anaesthetic gases in the human body, thus extending a well known four compartment model from literature. Real time data is used for the simulation, from which the sevoflurane concentration is extracted. The exhaled concentrations from the model and the data are compared. A literature model is extended with ratios of compartmental perfusion, the introduction of coefficients representing diffusion and the change of the solubility coefficient. The extended and the literature model are applied to five training data sets, with prior optimization of the parameters that have the most impact. Afterwards, both models are validated on five additional data sets. The results of the extended model are mostly better than the results of the literature model. Therefore, the extensions improve the model for the used data, but need to be validated with a larger amount of data.

The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles (UAVs) and Gaussian processes (GPs) to reduce the complexity of this task. Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. The most promising candidates are then integrated into an informative path-planning algorithm tasked with performing an efficient UAV-based cellular network mapping. The algorithm combines the physics-informed GP models with Bayesian optimization and is developed and tested in a hardware-in-the-loop simulation. The quantitative evaluation of the mean functions and the informative path-planning simulation are based on real-world measurements of the 5G reference signal received power (RSRP) in a cellular 5G-SA campus network at the Port of Lübeck, Germany. These measurements serve as ground truth for both evaluations. The evaluation results demonstrate that using an appropriate mean function can result in an enhanced prediction accuracy of the GP model and provide a suitable basis for informative path planning. The subsequent informative path-planning simulation experiments highlight these findings. For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials.

Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.

The reliable detection of muscle contractions in real-time is important for many applications. Both for prosthesis control and in the field of human-computer interaction, the physiological commands of the user must be recognized. However, conventional methods such as the electromyography are susceptible to interferences. A particularly robust method is the electrical impedance myography. Especially the temporal changes of the bioimpedance phase response are of interest for muscle activity monitoring. However, available wearable measurement systems are not capable of detecting muscle contractions in real-time to control prostheses or human-computer interaction devices. This work presents the development and metrological characterization of a wearable real-time bioimpedance spectrometer for the detection of muscle contractions. It can record the frequency responses in the range of 20-230 kHz from two antagonistic muscles. The sampling rate of 25 impedance spectra per second and per channel provides sufficient temporal resolution for many applications. Phantom measurements show that the statistical errors are below 1 % for the magnitude and below 0.4° for the phases, which is sufficient for electrical impedance myography. This system is used to perform first subject measurements. For the first time these measurements demonstrate the temporal impedance behavior and frequency responses of two antagonistic muscles during contraction. In addition, the directional dependence of the electrical impedance myography during a muscle contraction is investigated for the first time. The presented measurement system and novel measurement approaches are promising for many electrical impedance myography applications, especially for reliable muscle contraction detection e.g. in prosthetics or human-computer interaction applications.