Time-Frequency Analysis of Femoral and Carotid Arterial Doppler Signals

AbstractIn this study, the short time Fourier transform, continuous wavelet transform (CWT) and S-transform have been used for spectral analysis of the carotid and femoral arteries Doppler signal. Each of these methods can represent the temporal evolution of Doppler spectra know as the sonograms. Time-frequency analysis by S-transform presents a linear resolution that surpasses the problem of Fourier Transform by a slipping window (STFT) of fixed length and also corrects phase concept in the wavelet transform for the analysis of non-stationary signals. This transform provides a very suitable space for extracting features and the localization of discriminating information in time and frequency in Doppler ultrasonic signals. The sonograms have been then used to compare the methods in terms of their frequency resolution and effects in determining the stenosis of carotid and femoral arteries

Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements

Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating the cardiovascular system, and is an integral component of intensive care units, obstetrics wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive methods such as Pulmonary artery catheter or transoesophageal echocardiography. However, Doppler ultrasound scan acquisition requires a highly experienced operator and can be very challenging. Machine learning solutions that quantify and guide the scanning process in an automatic and intelligent manner could overcome these limitations and lead to routine monitoring. Development of such methods is the primary goal of the presented work. In response to this goal, this thesis proposes a suite of signal processing and machine learning techniques. Among these is a new and real-time method of maximum frequency envelope estimation. This method, which is based on image-processing techniques and is highly adaptive to varying signal quality, was developed to facilitate automatic and consistent extraction of features from Doppler ultrasound measurements. Through a thorough evaluation, this method was demonstrated to be accurate and more stable than alternative state-of-art methods. Two novel real-time methods of beat segmentation, which operate using the maximum frequency envelope, were developed to enable systematic feature extraction from individual cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram machine, and are fully automatic, real-time and highly resilient to noise. These qualities are not available in existing methods. Extensive evaluation demonstrated the methods to be highly successful. A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of- the-art image recognition classification method, hitherto undocumented for Doppler ultrasound analysis, was shown to be superior to more traditional modelling approaches. These contributions facilitated the design of two innovative types of feedback. To reflect beneficial probe movements, which are otherwise difficult to distinguish, a regression model to quantitatively score ultrasound measurements was proposed. This feedback was shown to be highly correlated with an ideal response. The second type of feedback explicitly predicted beneficial probe movements. This was achieved using classification models with up to five categories, giving a more challenging scenario than those addressed in prior disease classification work. Evaluation of these, for the first time, demonstrated that Doppler scan information can be used to automatically indicate probe position. Overall, the presented work includes significant contributions for Doppler ultrasound analysis, it proposes valuable new machine learning techniques, and with continued work, could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic monitoring

Experimental investigations of two-phase flow measurement using ultrasonic sensors

This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow

Impact of alpha adrenergic and myogenic control on forearm vasomotor properties

We tested the hypotheses that forearm vascular compliance (C) but not resistance (R) would be influenced by myogenic stimuli, and changing (A) forearm transmural pressure (TP) would influence the effect of a-adrenergic input on C and R. Continuous forearm blood flow was measured during Norepinephrine (NE; a-agonist) and during concurrent NE and Phentolamine (PH; a-antagonist) infusion with the arm above and below heart level (n=10). C was inversely related to TP (p\u3c0.05). NE decreased C and increased R (p\u3c0.05). PH abolished these responses. The effect of NE on AC was greater with the arm above versus below heart level (p\u3c0.05), while AR was only observed with the arm below the heart (p\u3c0.05). Conclusions: Myogenic changes affect forearm vascular C independent of changes in R. Alpha -adrenergic activation reduces C and increases R. Furthermore, with NE, AC requires a high starting value of C, while AR occurs under high T

Análisis y aplicación de técnicas ultrasónicas para ensayos no destructivos en materiales no homogéneos

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