Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Experimental and statistical investigation of Australian native plants for second-generation biodiesel production

This work explores the potential of Australian native plants as a source of second-generation biodiesel for internal combustion engines application. Biodiesels were evaluated from a number of non-edible oil seeds which are grow naturally in Queensland, Australia. The quality of the produced biodiesels has been investigated by several experimental and numerical methods. The research methodology and numerical model developed in this study can be used for a broad range of biodiesel feedstocks and for the future development of renewable native biodiesel in Australia.

Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

Institutional problems of the emerging scientific community in Iran

This article attempts to explore the concept of scientific community at the macro-national level in the context of Iran. Institutionalisation of science and its professional growth has been constrained by several factors. The article first conceptualises the notion of science community as found in the literature in the context of Iran, and attempts to map through some indicators. The main focus, however, lies in mapping some institutional problems through empirical research. This was undertaken in 2002–04 in order to analyse the structure of the scientific community in Iran in the ‘exact sciences’ (mathematics, physics, chemistry, biology and earth sciences). The empirical work was done in two complementary perspectives: through a questionnaire and statistical analysis of it, and through semistructured interviews with the researchers. There are number of problems confronting scientists in Iran. Facilities provided by institutions is one of the major problems of research. Another is the tenuous cooperation among scientists. This is reported by most of the researchers, who deplore the lack of cooperation among their group. Relationships are mostly with the Ph.D. students and only marginally with colleagues. Our research shows that the more brilliant the scientists, the more frustrated they are from scientific institutions in Iran. Medium-range researchers seem to be much happier about the scientific institution to which they belong than the brighter scholars. The scientific institutions in Iran seem to be built for the needs of the former rather than the latter. These institutions seem not to play a positive role in the case of the best scientists. On the whole, many ingredients of the scientific community, at least at its inception, are present among Iranian scientists: the strong desire for scientific achievement in spite of personal, institutional and economic problems.