A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In 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

Encrypting A 7.88ghz Frequency Message Within A Chaotic Carrier by Optical Feedback

A new laser system is suggested and experimentally verified as a chaotic transmitter for a secure optical communication system. The laser source kind is a distributed feedback with a peak wavelength 1310nm and maximum power 5mW. A doubly external cavity with 85cm of length is constructed via air. Chaotic signal is achieved successfully after the laser reach of coherence collapse, with a very wide band spectrum (12GHz). This value is capable to increase subjecting to several parameters based on optical feedback (OFB) such as laser current operating level, beam focusing, polarization control, etc. In order to test a message hiding possibility, a frequency message is modulated directly into the laser, which is connected with the laser source using a bias tee. For the free running (solitary) semiconductor laser, the maximum available direct current modulation is: 3GHz/mA, while this value can be increased by this technique. This gives the possibility for very high modulation values and increasing data package volume that can send securely in the applications that requires immunity

Estimating the permeability of reservoir sandstones using image analysis of pore structure

In this thesis, a method is developed for predicting the permeabilities of a core using only a small number of SEM images, without resorting to computationally intensive procedures. The pore structure is idealised as consisting of a cubic network of pore tubes having an arbitrary distribution of cross-sectional areas and shapes. The areas and perimeters of the individual pores are estimated from image analysis of scanning electron micrographs of thin sections, with appropriate stereological corrections introduced to infer the true cross sections of the pores. Effective medium theory is used to find the effective single-tube conductance, based on the measured distribution of individual conductances, thereby allowing a prediction of the permeability. The methodology has been applied to several reservoir sandstones from the North Sea, and also an outcrop sample from Cumbria, UK, yielding predictions that fall within a factor of two of the laboratory measurements in most cases. The procedure, although based on Kirkpatrick's intrinsically isotropic effectivemedium approximation, is not only capable of yielding reasonably accurate estimates of the permeabilities, but also gives a qualitatively correct indication of the anisotropy ratio. It also found that the use of an Bernasconi's anisotropic effective-medium approximation does not lead to a systematic improvement in the results, perhaps because the samples used in this study were insufficiently anisotropic for the approaches to yield different results. The validity of the effective medium approximation was also tested against exact pore network calculations. For the rocks examined in this study, with pore conductance distributions having log-variances less than 3, the effective medium approximation was found to be accurate to within a few percent.Open Acces

Unsupervised learning of Arabic non-concatenative morphology

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology