Model Predictive Valve Control for Lung Pressure Profile Tracking Assistance

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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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/ccta60707.2024.10666580
Primary Topic
Hydraulic and Pneumatic Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Model Predictive Valve Control for Lung Pressure Profile Tracking Assistance

M. C. Thompson, Ann‐Marie Hughes, R. Marchbanks, Nicole O’Brien et al.
Hydraulic and Pneumatic Systems
article

Model Predictive Valve Control for Lung Pressure Profile Tracking Assistance

M. C. Thompson, Ann‐Marie Hughes, R. Marchbanks, Nicole O’Brien, Christopher Freeman, Anthony A. Birch
article en

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.

University Hospital Southampton NHS Foundation Trust (GB), University of Southampton (GB)
Openalex Percentile: Top 85%
Hydraulic and Pneumatic Systems
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