Data-driven Modelling of Cerebrospinal Fluid Dynamics for Hardware-in-the-Loop Shunt Testing

Hydrocephalus is a medical condition characterized by the disturbed dynamics of cerebrospinal fluid (CSF) and its excessive accumulation in the brain ventricles. In contemporary therapy, a shunt system is implanted that drains CSF from the ventricles into the peritoneal space. While various types of shunt systems exist, they are essentially all based on passive mechanical pressure valves that are driven by the external pressure gradient. This limits the efficacy of these shunts and complications such as over- and underdrainage may occur. To improve the therapy of hydrocephalus, we are working towards intelligent mechatronic shunt systems that are capable of monitoring vital signs and adapting CSF drainage according to the patient’s actual needs. The development of such a smart shunt system requires a sophisticated model of the CSF dynamics for controller design and system testing. Your task will be to develop and evaluate this model using an extensive data set of physiological time-series derived from ovine in-vivo trials. The model will subsequently be deployed on an embedded computing hardware to enable real-time simulations of CSF dynamics. The final goal will be the validation of the deployed model for hardware-in-the-loop shunt testing, such that in-vivo shunt tests can be reproduced and animal investigations minimized.

Hydrocephalus is a medical condition characterized by the disturbed dynamics of cerebrospinal fluid (CSF) and its excessive accumulation in the brain ventricles. In contemporary therapy, a shunt system is implanted that drains CSF from the ventricles into the peritoneal space. While various types of shunt systems exist, they are essentially all based on passive mechanical pressure valves that are driven by the external pressure gradient. This limits the efficacy of these shunts and complications such as over- and underdrainage may occur. To improve the therapy of hydrocephalus, we are working towards intelligent mechatronic shunt systems that are capable of monitoring vital signs and adapting CSF drainage according to the patient’s actual needs.

The development of such a smart shunt system requires a sophisticated model of the CSF dynamics for controller design and system testing. Your task will be to develop and evaluate this model using an extensive data set of physiological time-series derived from ovine in-vivo trials. The model will subsequently be deployed on an embedded computing hardware to enable real-time simulations of CSF dynamics. The final goal will be the validation of the deployed model for hardware-in-the-loop shunt testing, such that in-vivo shunt tests can be reproduced and animal investigations minimized.

• Literature research on hydrocephalus and cerebrospinal fluid dynamics • Design and evaluation of a dynamical system model using in-vivo data • Deployment of the model on a real-time computing hardware • Validation of the model for hardware-in-the-loop shunt testing

• Literature research on hydrocephalus and cerebrospinal fluid dynamics
• Design and evaluation of a dynamical system model using in-vivo data
• Deployment of the model on a real-time computing hardware
• Validation of the model for hardware-in-the-loop shunt testing

Fabian Flürenbrock (ffluerenb@ethz.ch)

Fabian Flürenbrock (ffluerenb@ethz.ch)