We confirm the presence of cardiovascular autonomic dysfunction in a large group of SCA2 patients.

The provision of sufficient chest compression is among the most important factors influencing patient survival during cardiopulmonary resuscitation (CPR). One approach to optimize the quality of chest compressions is to use mechanical-resuscitation devices. The aim of this study was to compare a new device for chest compression (corpuls cpr) with an established device (LUCAS II). We used a mechanical thorax model consisting of a chest with variable stiffness and an integrated heart chamber which generated blood flow dependent on the compression depth and waveform. The method of blood-flow generation could be changed between direct cardiac-compression mode and thoracic-pump mode. Different chest-stiffness settings and compression modes were tested to generate various blood-flow profiles. Additionally, an endurance test at high stiffness was performed to measure overall performance and compression consistency. Both resuscitation machines were able to compress the model thorax with a frequency of 100/min and a depth of 5 cm, independent of the chosen chest stiffness. Both devices passed the endurance test without difficulty. The corpuls cpr device was able to generate about 10-40% more blood flow than the LUCAS II device, depending on the model settings. In most scenarios, the corpuls cpr device also generated a higher blood pressure than the LUCAS II. The peak compression forces during CPR were about 30% higher using the corpuls cpr device than with the LUCAS II. In this study, the corpuls cpr device had improved blood flow and pressure outcomes than the LUCAS II device. Further examination in an animal model is required to prove the findings of this preliminary study.

ZusammenfassungDie frühe Behandlung mit Extrakorporalen Kreislauf-Unterstützungssystemen bei Patienten mit kardiogenem Schock wirkt sich positiv auf den weiteren Verlauf aus und kann einem Multi-Organversagen entgegenwirken. Um eine frühe Behandlung zu ermöglichen, ist eine kontinuierlicheÜberwachung des Patienten durch geschulte Kardiotechniker notwendig. Unter Notfallbedingungen kann eine ungeteilte Aufmerksamkeit für den Patienten nicht garantiert werden womit es zu Behandlungsfehlern kommen kann.Durch AbstractPatients suffering from cardiogenic shock may benefit with an early application of a portable Extracorporeal Circulatory Support System (ECSS) preventing multi organ failure. This however requires the presence and constant supervision of the patient by trained personal at the emergency site. Under these circumstances full attention to the patient may not be guaranteed and operation errors may occur.With the automation of the portable ECSS optimal perfusion may be achieved with minimal workload for the human operator allowing the safe transportation of the patient to the hospital.The focus of this thesis is the development of an adaptive and robust control system that regulates perfusion based on online data of the patient. While the system needs to be highly dynamic, so that it is able to adapt to different situations, it must ensure maximal patient safety at all times.To develop such control system first an animal model was used to analyze the type of signals acquired during extracorporeal circulation. This information was used as a reference for the creation of a mathematical model. The model includes a cardiovascular system undergoing extracorporeal circulation, a gas exchange model and a medication model. This was integrated into a simulation system that could be used for the creation and evaluation of the designed controller.Fuzzy logic was considered as a control mechanism allowing the easy creation of rules based on the knowledge of trained perfusionists. Since patient pre-conditions and reactions will be different from one case to another an adaptive mechanism is proposed to modify the existing controller and adapt to the specific needs of the patient.A software framework was developed allowing a fast implementation of the control system. This framework was created not only focusing on the automation of the ECSS but also to serve as a basis for the development of control systems for other medical devices with similar requirements.Several simulations are presented showing the performance of the fuzzy controller with the proposed adaptive mechanism. Additional simulations show the response of the designed ECSS controller under different patient scenarios.vii Acknowledgments I want to give thanks to my advisor Prof. Dr. Alois Knoll for giving me the opportunity of working in this thesis, for his support and confidence. I would also like to thank Prof. Dr. Robert Bauernschmitt for taking the time to review my work and acting as a second advisor. From the department of Robotics and Embedded Systems ...

Abstract-Cardiogenic shock due to myocardial infarction is still associated with a high mortality of more than 60%. Early treatment of patients having a low cardiac output with an extra-corporal circulatory support system (ECSS) could prevent multi-organ failure. Current heart-lung machines (HLM) need to be constantly supervised and operated by trained personnel, making it a difficult task to use a HLM in emergency situations. For this project we used the Lifebridge B 2 T as an existing portable, modular, rapidly available "plug and play" mechanical circulatory support system. We present the AutoMedic platform for the development of a mobile, autonomous and self-controlling ECSS. This framework was established in order to give medical doctors and engineers a common ground for the design of a controller to automate a ECSS. Such a controller has to be capable of adapting to the individual needs of each patient. Besides, such a system needs to be robust, reliable and must constantly guarantee patient safty.

Abstract-For many classification or controlling problems a set of training data is available. To make best use of this training data it would be ideal to feed the data into a learning algorithm, which then outputs a finished, trained fuzzy controller, that is able to classify or control the original system. For the FUZZ-IEEE 2012 a competition was proposed to predict future volumes sold per day in a certain gas station. The training data includes a collection of gas prices at the current and the competitor's gas station and the according volume sold on every consecutive day in a period of about one year. This training data was analyzed and fit to a fuzzy learning algorithm based on the Münsteraner Optimisation System. As a base point a mean value comparison is used and then different features as fuzzy inputs are tested. Also different fuzzy set widths and and sequence of commands are compared. The final controller chosen shows promising results in the test with left out training data sets. Final results still have to be shown with the test data of the competition.

3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutional network named PoseG-raphNet for 3D human pose regression from 2D poses. Our network uses an adaptive adjacency matrix and kernels specific to neighbor groups. We evaluate our model on the Hu-man3.6M dataset which is a standard dataset for 3D pose estimation. Our model's performance is close to the state-of-theart, but with much fewer parameters. The model learns interesting adjacency relations between joints that have no physical connections, but are behaviorally similar.

A simulation environment was developed as a tool for the design of a fuzzy controlled perfusion. This consists of a mathematical model of the cardiovascular system connected to a heart-lung machine. An existing cardiovascular model extracted from the PHYSIOME database was used and extended with the model of a HLM. A visual interface was created to simulate real-time patient data where specific scenarios can be evaluated. Preliminary results are shown where the model was adjusted with experimental data and a fuzzy controller was activated to test its performance.

Mental fatigue is usually accompanied by drops in task performance and reduced willingness for further exertion. A value-based theoretical account may help to explain such negative effects. In this view, mental fatigue influences perceived costs and rewards of exerting effort. However, no formal mathematical framework has yet been proposed to model and quantitatively estimate the effects of mental fatigue on subjective evaluations of effort expenditure, subject to possibly imperfect self-perceptions of internal fatigue states. We proposed a mathematical framework to model human cognitive effort allocations, assuming mental fatigue states are partially observable with semi-Markov dynamics. We modeled effort allocation decisions as consistent with the goal of maximizing cumulative subjective values over a given time horizon. We analyzed the proposed model structure and developed an estimation method to identify subjective values and hidden fatigue dynamics, which can be based on self-reports, psychophysiological indices, and behavioral effects associated with fatigue. The modeling and estimation method was tested using a simulated n-back task under a free choice paradigm, with model parameters fine-tuned from past studies. The proposed approach was able to recapitulate task performance and engagement patterns observed under mental fatigue. This work advances a reward/cost trade-off account for explaining the principles of effort exertion and suggests new avenues for both theoretically and empirically relevant understandings of how cognitive operations are affected by mental fatigue.