Model predictive valve control for lung pressure profile tracking assistance

Abstract

Over 12,000 people in the UK are diagnosed with a brain tumour every year. Current diagnostic methods are expensive and typically invasive, meaning that early, widespread screening is not possible. This paper develops a control set-up that enables a new, low-cost, non invasive eardrum sensor to provide accurate intracranial pressure measurements, thereby facilitating more effective diagnosis. The controller works by assisting participants to accurately track airway pressure profiles via blowing into a tube. This has the effect of removing blood flow fluctuations that would otherwise corrupt the eardrum sensor readings used to compute intracranial pressure.

A new experimental hardware set-up, identification process and model predictive control approach are developed to compute the optimal sequence of valve positions, and experimental results show a 21% improvement in pressure profile tracking compared with existing clinical methods.

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Identifiers

ORCID for Chris Freeman: orcid.org/0000-0003-0305-9246

ORCID for Ann-Marie Hughes: orcid.org/0000-0002-3958-8206

ORCID for Tony Birch: orcid.org/0000-0002-2328-702X

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