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Until this day, the drainage of cerebrospinal fluid, as necessary in case of an acute increased intracranial pressure, is conducted manually by adjusting the hydrostatic height of an external drainage bag. The associated problems with the manual open-loop control strategy are an increasing pressure error over time periods involving no correction and the inherent risk of an overdrainage, which may occur, for example, after changes of the patient’s upper body inclination angle. In this paper, an automatic control strategy is suggested to alleviate these problems thereby increasing the patient’s safety and the overall quality of the therapy. The automatic controller presented in this paper is designed for our newly developed intelligent external ventricular drainage system. The proposed controller has to guarantee robustness and performance in face of uncertain patient intracranial dynamics and nonlinearities associated with the actuator. The controller is thus designed to guarantee robust performance using a mixed uncertainty modeling approach and extended by a self-scheduling approach to compensate for input nonlinearities. Controller performance is validated in nonlinear simulations, an experimental test setup, and animal experiments, involving pigs with an artificially induced hydrocephalus. [ABSTRACT FROM AUTHOR]

In humans, intracranial pressure (ICP) is not only influenced by pathology, but also by orientation in space and body movements. Therefore, it is proposed to measure ICP dynamics and body acceleration simultaneously. An algorithm for acceleration analysis was developed to monitor orientation in space and allow more accurate examination of ICP dynamics during quiet periods. [ABSTRACT FROM PUBLISHER]