The development of an algorithm to track blood pressure accurately based on the pulse transit time method

Oscillometric blood pressure (BP) monitors are currently used to diagnose hypertension both in home and clinical settings. These monitors take BP measurements once every 15 minutes over a 24 hour period and provide a reliable and accurate system that is minimally invasive. Although intermittent cuff measurements have proven to be a good indicator of BP, a continuous BP monitor is highly desirable for the diagnosis of hypertension and other cardiac diseases. However, no such devices currently exist. A novel algorithm has been developed based on the Pulse Transit Time (PTT) method, which would allow non-invasive and continuous BP measurement. PTT is defined as the time it takes the BP wave to propagate from the heart to a specified point on the body. After an initial BP measurement, PTT algorithms can track BP over short periods of time, known as calibration intervals. After this time has elapsed, a new BP measurement is required to recalibrate the algorithm. Using the PhysioNet database as a basis, the new algorithm was developed and tested using 15 patients, each tested 3 times over a period of 30 minutes. The predicted BP of the algorithm was compared to the arterial BP of each patient. It has been established that this new algorithm is capable of tracking BP over 12 minutes without the need for recalibration, using the BHS standard, a 100% improvement over what has been previously identified. The algorithm was incorporated into a new system based on its requirements and was tested using three volunteers. The results mirrored those previously observed, providing accurate BP measurements when a 12 minute calibration interval was used. This new system provides a significant improvement to the existing method allowing BP to be monitored continuously and non-invasively, on a beat-to-beat basis over 24 hours, adding major clinical and diagnostic value.

Immune and stress factors in the pathophysiology of the mdx mouse model of Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is a fatal multi-system neuromuscular disease caused by loss of dystrophin. The loss of dystrophin from membranes of contractile muscle cells and the dysregulation of the DAPC, induces chronic inflammation due to tissue necrosis and eventual replacement with collagen which weakens muscular force and strength. Dystrophin deficiency may cause under-diagnosed features of DMD include mood disorders such as depression and anxiety and dysfunction of the gastrointestinal tract. The first study in the thesis examined mood in the dystrophin-deficient mdx mouse model of DMD and examined the effects of the tri-cyclic antidepressant, amitriptyline on behaviours. Amitriptyline had anti-depressant and anxiolytic effects in the mdx mice possibly through effects on stress factors such as corticotrophin-releasing factor (CRF). This antidepressant also reduced skeletal muscle inflammation and caused a reduction in circulating interleukin (IL)-6 levels. In the second and third studies, we specifically blocked IL-6 signalling and used Urocortin 2, CRFR2 agonist to investigate their potential as therapeutic targets in mdx mice pathophysiology. Isometric and isotonic contractile properties of the diaphragm, were compared in mdx mice treated with anti IL-6 receptor antibodies (anti IL-6R) and/or Urocortin 2. Deficits in force production, work and power detected in mdx mice were improved with treatment. In study three I investigated contractile properties in gastrointestinal smooth muscle. As compared to wild type mice, mdx mice had slower faecal transit times, shorter colons with thickened muscle layers and increased contractile activity in response to recombinant IL-6. Blocking IL-6 signalling resulted in an increase in colon length, normalised faecal output times and a reduction in IL-6-evoked contractile activity. The findings from these studies indicate that for both diaphragm and gastrointestinal function in a dystrophin-deficient model, targeting of IL-6 and CRFR2 signalling has beneficial therapeutic effects.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.