Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005. / Most of the relevant and severe congenital cardiac malfunctions can be recognized in the neonatal period of a child’s life. The delayed recognition of a congenital heart defect may have a serious impact on the long-term outcome of the affected child. Experienced cardiologists can usually evaluate heart murmurs with a high sensitivity and specificity, although non-specialists, with less clinical experience, may have more difficulty. Although primary care physicians frequently encounter children with heart murmurs most of these murmurs are innocent. The aim of this project is to design an automated algorithm that can assist the primary care physician in screening and diagnosing pediatric patients with possible cardiac malfunctions. Although attempts have been made to automate screening by auscultation, no device is currently available to fulfill this function. Multiple indicators of pathology are nonetheless available from heart sounds and were elicited using several signal processing techniques. The three feature extraction algorithms (FEA’s) developed respectively made use of a Direct Ratio technique, a Wavelet analysis technique and a Knowledge based neural network technique. Several implementations of each technique are evaluated to identify the best performer. To test the performance of the various algorithms, the clinical auscultation sounds and ECG-data of 163 patients, aged between 2 months and 16 years, were digitized. Results presented show that the De-noised Jack-Knife neural network can classify 163 recordings with a sensitivity and specificity of 92 % and 92.9 % respectively. This study concludes that, in certain conditions, the developed automated auscultation algorithms show significant potential in their use as an alternative evaluation technique for the classification of heart sounds in normal (innocent) and pathological classes.

CAPES / Esta tese propõe uma metodologia baseada em Ontologias e técnicas de Inteligência Artificial para apoio ao diagnóstico e ao processo de ensino-aprendizagem em Fisioterapia Neuropediátrica. Nesta área são escassas as medidas objetivas que permitam quantificar o diagnóstico e a evolução de um paciente. O diagnóstico é limitado a informar em quais meses do desenvolvimento motor normal um paciente pode ser classificado, baseando-se na experiência subjetiva do fisioterapeuta. Neste trabalho foram utilizados métodos formais para a aquisição e representação do conhecimento de especialistas da área. Conflitos de opiniões foram tratados sistematicamente e o conhecimento foi representado por uma Ontologia. Esta gerou um conjunto de regras de classificação a partir do qual três abordagens foram desenvolvidas: um sistema especialista crisp, um fuzzy e um baseado em modelos determinísticos. O primeiro teve um desempenho não condizente com a realidade do problema. O segundo se mostrou também inadequado. A abordagem com modelos determinísticos se mostrou adequada para classificar um paciente com diferentes graus de pertinência a múltiplos meses do desenvolvimento motor. Os resultados utilizando esta metodologia sugerem que o mesmo é capaz de simular objetivamente o diagnóstico fornecido por especialistas ao analisarem casos reais, em 90% dos casos. Uma extensão do trabalho foi a utilização da Ontologia em uma ferramenta de suporte ao processo de ensino-aprendizagem deste conteúdo em Fisioterapia. Esta abordagem mostrou resultados satisfatórios, tendo sido utilizada tanto por profissionais quanto por alunos, mostrando o seu potencial como recurso multimídia de ensino. 85% dos profissionais entrevistados concordaram fortemente sobre o potencial da ontologia para se tornar uma nova forma de contribuição ao processo de ensino-aprendizagem deste conteúdo. As principais contribuições desta tese são: a gestão eficiente do conhecimento em um domínio cuja característica é a fraca sistematização e a subjetividade; metodologias para apoio à quantificação do diagnóstico do paciente neuropediátrico; e o desenvolvimento de uma ferramenta para suporte ao ensino baseado em uma Ontologia. / This thesis proposes a new methodology based on ontologies and artificial intelligence techniques to support the diagnosis and the teaching-learning process in neuropediatric physiotherapy. In this area, standardized and objective measurements to quantify the diagnosis are difficultly found. The diagnosis is limited to inform in which months of the normal motor development a patient can be classified, based upon only on the subjective experience of the physiotherapist. In this work formal methods for knowledge acquisition and representations were used. Possible divergences of opinions between experts were systematically treated, and the acquired knowledge was represented as an ontology. Such ontology generated a set of classification rules from which three different approaches for diagnosis were developed: a crisp expert system, a fuzzy system, and another approach based on deterministic models. The crisp expert system did not accomplish to the problem. The fuzzy approach was not adequate too. The last approach was shown to be adequate for classifying a given patient with different degrees of membership to several months of the motor development. Results using this methodology suggested that it is capable of simulating objectively the diagnosis from human experts when analyzing real-world cases, in 90% of the cases. An extension of this work is the use of the developed ontology in a tool to support the teaching-learning process of neuropediatric physiotherapy. Such approach revealed fairly satisfactory. It was tested by professionals and students, and both found it promising as a multimedia educational resource. 85% strongly agreed about the ontology potential to be used as a tool for teaching-learning process. Overall, the main contributions of this thesis are: efficient knowledge management in a domain with weak standardization and high subjectivity of expert knowledge; methodologies for supporting the quantification of the diagnosis of a neuropediatric patient; and the development of an ontology-based multimedia tool for educational purposes.

PhD (Health Sciences) / Department of Advanced Nursing Science / Background: Family-centred approaches to Prevention of Mother-to-Child Transmission (PMTCT) interventions present an important direction for sustainability and prevention of pediatric infections while improving overall family health. Despite numerous opportunities to sustain and expand the existing PMTCT interventions, Mother-to-Child Transmission (MTCT) still occurs. This is evidenced by the number of under-five children who are admitted in hospital being infected by the Human Immunodeficiency Virus (HIV) between the ages of 6 weeks to 18 months, whereas the Polymerase Chain Reaction (PCR) results was non-reactive at six weeks. Purpose: The purpose of this study was to develop a strategy to enhance family-centered interventions for PMTCT sustainability in the selected districts of Limpopo Province, South Africa. Phase 1: The study was conducted in phases. In Phase 1, which was empirical, the following objectives: to explore the risks that contribute to MTCT between the ages of 6 weeks and 18 months; to explore the perceptions of family members regarding family support in PMTCT interventions; and to explore the factors that affect the provision of family support in PMTCT interventions. Phase 2: was development of the strategy and validation of the strategy. Methods: The exploratory sequential mixed method was used to conduct the study, where qualitative data were collected and analyzed first; followed by collecting, analyzing and interpreting the quantitative data. The population comprised the following groups: mothers of babies between 6 weeks and 18 months who are living with HIV/AIDS, family members were represented by male partners, grandmothers or mother’s in_-law and health care professionals working at the PHC Heath Centers v or clinics rendering PMTCT services. In the qualitative design, participants were selected by non-probability purposive sampling and data were collected through one-to-one interview and focus group discussions. Data were analyzed utilizing the open-coding method. In the quantitative design, participants were selected by using simple random sampling and data were collected by means of self-administered survey questionnaires with structured close-and open-ended questions. The population were midwives from Capricorn, Mopani and Vhembe districts PHC clinic. Data were analyzed using the Statistical Package for the Social Sciences (SPSS), Version 22 and descriptive statistics. In Phase 2, findings from the data were used to develop an intervention strategy. The strategy was developed through the use of Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis. The developed strategy was validated by using a quantitative design. / NRF