Thorsten Janisch

Enrichment of a diving computer with body sensor network data

Decompression algorithms in hyperbaric applications currently usually base on information about the ambient pressure in a temporal course. However, the impact of other factors like temperature or physical activity is well documented in literature. Therefore, we elaborated a prototypic setup, which is not only able to enrich the decompression algorithms run on a diving computer by this data, but also store this information for successive data mining.

Decentralized safety concept for closed-loop controlled intensive care : Supervision of a blood pump during extracorporeal circulation

Abstract: This paper presents a decentralized safety concept for networked intensive care setups, for which a decentralized network of sensors and actuators is realized by embedded microcontroller nodes. It is evaluated for up to eleven medical devices in a setup for automated acute respiratory distress syndrome (ARDS) therapy. In this contribution we highlight a blood pump supervision as exemplary safety measure, which allows a reliable bubble detection in an extracorporeal blood circulation. The approach is validated with data of animal experiments including 35 bubbles with a size between 0.05 and 0.3 ml. All 18 bubbles with a size down to 0.15 ml are successfully detected. By using hidden Markov models (HMMs) as statistical method the number of necessary sensors can be reduced by two pressure sensors.

Physiological closed-loop control of mechanical ventilation and extracorporeal membrane oxygenation.

Abstract A new concept is presented for cooperative automation of mechanical ventilation and extracorporeal membrane oxygenation (ECMO) therapy for treatment of acute respiratory distress syndrome (ARDS). While mechanical ventilation is continuously optimized to promote lung protection, extracorporeal gas transfer rates are simultaneously adjusted to control oxygen supply and carbon dioxide removal using a robust patient-in-the-loop control system. In addition, the cooperative therapy management uses higher-level algorithms to adjust both therapeutic approaches. The controller synthesis is derived based on the introduced objectives, the experimental setup and the uncertain models. Finally, the autonomous ARDS therapy system capabilities are demonstrated and discussed based on in vivo data from animal experiments.

Fault Identification in a Blood Pump Using Neural Networks

This paper compares two fault identification implementations based on a neural network and a model based approach. Our worked example is the detection of gas bubbles in the pump head of a centrifugal blood pump. We focus on algorithms applicable on minimal sensor data with a reasonable implementation effort. The approaches were restricted to the desired blood flow and the measured rotational speed of the pump. We evaluated both implementations with data from an ECMO system.