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Abstract-Patient monitors in intensive care units trigger alarms if the state of the patient deteriorates or if there is a technical problem, e.g. loose sensors. Monitoring systems have a high sensitivity in order to detect relevant changes in the patient state. However, multiple studies revealed a high rate of either false or clinically not relevant alarms. It was found that the high rate of false alarms has a negative impact on both patients and staff. In this study we apply data mining methods to reduce the false alarm rate of monitoring systems. We follow a multi-parameter approach where multiple signals of a monitoring system are used to classify given alarm situations. In particular we focus on five alarm types and let our system decide whether the triggered alarm is clinically relevant or can be considered as a false alarm. Several classification algorithms (Naive Bayes, Decision Trees, SVM, kNN and Multi-Layer Perceptron) were evaluated. For training and test sets a subset of the freely available MIMIC II database was used. Alarm-specific classification accuracy was between 78.56% and 98.84%. Suppression rates for false alarms were between 75.24% and 99.23%. Classification results strongly depend on available training data, which is still limited in the intensive care domain. However, this study shows that data mining methods are useful and applicable for alarm classification.

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Extra-corporal Circulation Support Systems (ECCS) are used in cardiac surgery on a daily basis. Surgeons and perfusionists supervise patients' vital signals such as heart rate or blood pressure and ensure secure and errorless operation of the system. An automated ECCS can help to reduce the workload of human operators and can provide a patient with optimal perfusion. In our opinion a fuzzy controller is predestinated for autonomus heart-lung machine (HLM) control, since it allows a straightforward implementation of expert knowledge and is able to deal with inherently imprecise data. In this work we present a comprehensive approach towards intelligent HLM automation that uses fuzzy logic as a control methodology. This approach includes the development of a mechanical circulatory model, a purely virtual model, as well as animal experiments.

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 ...