This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse waveforms obtained by the SphygmoCor"??" device is then analysed by the unsupervised learning technique of Self Organising Maps (SOM). The map patterns indicate that the observed arterial pressures affect the wave shape in a similar way as predicted by the theoretical model. A database of continuous arterial pressure obtained by catheter line during sleep is used to derive supervised models that enable estimation of arterial pressures, based on the measured wave shapes. Independent component analysis (ICA) is also used in a supervised learning methodology to show the theoretical plausibility of separating the pressure signals from unwanted noise components. The accuracy and repeatability of the SphygmoCor?? device is measured and discussed. Alternative regression models are introduced that improve on the existing models in the estimation of central cardiovascular parameters from peripheral arterial wave shapes. Results of this investigation show that from the information in the wave shape, it is possible, in theory, to estimate the continuous underlying pressures within the artery to a degree of accuracy acceptable to the Association for the Advancement of Medical Instrumentation. This could facilitate a new role for non-invasive sphygmographic devices, to be used not only for feature estimation but as alternatives to invasive arterial pressure sensors in the measurement of continuous blood pressure.

This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse waveforms obtained by the SphygmoCor"??" device is then analysed by the unsupervised learning technique of Self Organising Maps (SOM). The map patterns indicate that the observed arterial pressures affect the wave shape in a similar way as predicted by the theoretical model. A database of continuous arterial pressure obtained by catheter line during sleep is used to derive supervised models that enable estimation of arterial pressures, based on the measured wave shapes. Independent component analysis (ICA) is also used in a supervised learning methodology to show the theoretical plausibility of separating the pressure signals from unwanted noise components. The accuracy and repeatability of the SphygmoCor?? device is measured and discussed. Alternative regression models are introduced that improve on the existing models in the estimation of central cardiovascular parameters from peripheral arterial wave shapes. Results of this investigation show that from the information in the wave shape, it is possible, in theory, to estimate the continuous underlying pressures within the artery to a degree of accuracy acceptable to the Association for the Advancement of Medical Instrumentation. This could facilitate a new role for non-invasive sphygmographic devices, to be used not only for feature estimation but as alternatives to invasive arterial pressure sensors in the measurement of continuous blood pressure.