Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/6466
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dc.contributor.advisorAlshawi, S-
dc.contributor.authorHolzhausen, Rudolf-
dc.date.accessioned2012-06-08T13:23:04Z-
dc.date.available2012-06-08T13:23:04Z-
dc.date.issued2011-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/6466-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractIn the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph.D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care.en_US
dc.language.isoenen_US
dc.publisherBrunel University Brunel Business School PhD Theses-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/6466/1/FulltextThesis.pdf-
dc.subjectECGen_US
dc.subjectDifferentialen_US
dc.subjectEinthovenen_US
dc.subjectQT intervalen_US
dc.subjectBasseten_US
dc.titleA clinical patient vital signs parameter measurement, processing and predictive algorithm using ECGen_US
dc.typeThesisen_US
Appears in Collections:Business and Management
Brunel Business School Theses

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