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dc.contributor.advisorAl-Jumaily, Ahmed
dc.contributor.advisorLowe, Andrew
dc.contributor.authorMa, Xiaoqi
dc.date.accessioned2012-07-31T22:14:29Z
dc.date.available2012-07-31T22:14:29Z
dc.date.copyright2012
dc.date.created2012
dc.date.issued2012-08-01
dc.identifier.urihttp://hdl.handle.net/10292/4551
dc.description.abstractThe oscillometric method is the most common technique used in commercial non-invasive blood pressure monitoring. In this technique, devices record the pressure oscillations in the cuff as the cuff pressure decreases from supra-systolic to sub-diastolic. The values for systolic pressure (SP) and diastolic pressure (DP) are determined by analyzing the shape of the oscillometric envelope. In many oscillometric algorithms, fixed percentile algorithms are used to determine SP and DP but their accuracy has been questioned. In this research an algorithm has been developed based on a beat-by-beat pattern recognition approach using time and frequency domain signal processing to extract features and an artificial neural network (ANN) is designed for classification of each beat as supra-systolic, sub-diastolic or in between. Normalized beat shape is successful at determining SP and DP and it also shows good agreement with recommended gold standard blood pressure auscultatory measurement.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectBlood Pressureen_NZ
dc.subjectNon-invasiveen_NZ
dc.subjectOscillometricen_NZ
dc.subjectArtificial neural networken_NZ
dc.subjectPattern recognitionen_NZ
dc.subjectCuff Pressureen_NZ
dc.titleNon-invasive blood pressure measurement algorithm for all age groupsen_NZ
dc.typeThesis
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Engineeringen_NZ
thesis.degree.discipline
dc.rights.accessrightsOpenAccess
dc.date.updated2012-07-31T11:04:28Z


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