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Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU)

Indera Putera, Siti Hazurah (2018) Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU). PhD thesis, University of Sheffield.

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Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU)_Siti Hazurah_Oct 2018.pdf
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Abstract

The Intensive Care Unit (ICU) is where the critically-ill are treated. The first 24-hours (‘the golden hours’) of treatment is crucial to determine patient’s recovery and survival, and mechanical ventilation plays a major role as the main life support system in the ICU. The efficiency of mechanical ventilation and its management strategy are assessed by observing the arterial blood gases (ABG), which are sampled every few hours using a catheter inserted into the patient’s artery. This procedure is invasive thus can only be performed a handful of times each day. The ICU also has an abundance of underutilized data which until recently can only be translated by expert clinicians, who unfortunately always have clinical responsibilities to undertake concomitantly. This thesis proposes a series of new fuzzy logic-based models with a new type of fuzzy sets (type-2), which have not been investigated before in this clinical setting, for the relative dead-space (Kd), the carbon-dioxide production (VCO2), and the shunt sub-components for the SOPAVent (Sheffield Simulation of Patients under Artificial Ventilation) system, which performs predictions of arterial blood gases non-invasively and automatically. The Kd model, the VCO2 model and the resulting overall SOPAVent model are validated with retrospective real ICU patient data obtained from the Sheffield Royal Hallamshire Hospital (UK). The SOPAVent model is also validated with newly obtained data from patients diagnosed with Faecal Peritonitis (FP), from the Sheffield Royal Hallamshire Hospital (UK). Results showed an improved prediction accuracy for the Kd and the VCO2 sub-components when compared to existing systems. The prediction capability of SOPAVent is also improved from previous models for arterial blood gases before and after ventilator settings changes are made. A second new simplified model for predicting ABG using ventilator settings is also proposed with excellent prediction outcomes. Additionally, this thesis also looks into Electrical Impedance Tomography (EIT) as a potential bedside monitoring tool for pulmonary functions. EIT has the ability to provide a non-invasive, portable, and a relatively low cost alternative to other medical imaging systems. This thesis details the development of the hardware for a compact 16-electrode EIT measurement system, with the objective for future pulmonary applications. A method to generate three-dimensional (3D) images of the lungs from two-dimensional (2D) medical images of the thorax is also proposed with the estimation of lung volumes being presented.

Item Type: Thesis (PhD)
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Automatic Control and Systems Engineering (Sheffield)
Depositing User: Mrs Siti Hazurah Indera Putera
Date Deposited: 04 Feb 2019 09:19
Last Modified: 04 Feb 2019 09:19
URI: http://etheses.whiterose.ac.uk/id/eprint/22144

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