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Title: Vital sign monitoring and analysis in acute coronary syndrome patients
Author: Vilakazi, Christina Busisiwe
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2012
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Distinguishing patients with acute coronary syndromes (ACS) within the very large proportion with suspected cardiac pain is a diagnostic challenge, especially in individuals without clear symptoms or electrocardiographic features. This thesis presents the development of a patient data fusion system that would ultimately be able to assist with early identification of patients with non-ST-elevation Acute Coronary Syndrome (NSTEACS) in the pre-hospital setting. In order to identify high-risk ACS patients, patients with ST changes need to be identified. A rule-based ST segment analysis technique was developed and validated on two publicly available databases. Even in subjects who are known to have myocardial ischaemia (MI), ST changes are not considered as a basis for a definitive diagnosis of individual episodes of ischaemia. A technique to differentiate between ST changes caused by MI and those caused by non-ischaemic ST changes such as body position changes or conduction changes was also developed. At the outset of this research there was no available database with continuous vital-sign data for recorded during the ambulance transport. A clinical study was set up to collect vitalsign data and ECG during ambulance transport. Additional parameters from the ECG such as heart rate variability that can be used for detecting ACS, particularly NSTEACS, were investigated. High-risk patients may be identified early by appropriate combination of vital-sign data, demographic information and ECG analysis. This thesis contributes to the literature on early identification of NSTEACS patient during the ambulance transportation. Firstly, it differentiate between ischaemic and non-ischaemic ST episodes using non-linear classifiers. pre-hospital data to ensure . Secondly, it investigates the diagnostic and prognostic value of heart rate variability in identifying high-risk NSTEACS patients in a pre-hospital setting. Thirdly, the thesis attempts to show how data fusion of vitalsign data and additional parameters from the ECG can be used to differentiate between noncardiac and NSTEACS patients in a pre-hospital setting. Lastly, the thesis also provides some insight into the data collection process in a pre-hospital setting.
Supervisor: Tarassenko, Lionel; Clifford, Gari Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Biomedical engineering ; Vital Sign Monitoring