Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009. / Pulmonary tuberculosis is a common and potentially deadly infectious disease, commonly affecting the respiratory area. Over one-third of the world’s population is infected with the tuberculosis bacterium. Since pulmonary tuberculosis damages the respiratory area, the sound properties of infected lungs differ from those of non-infected lungs. However, auscultation is often ruled out as a reliable diagnostic technique due to the random position and severity of damage to the lungs as well as requiring the personal and trained judgment of an experienced medical practitioner. This project investigates a possible improvement in the pulmonary diagnostic and treatment field by applying electronic and computer-aided sound analysis techniques to analyze respiratory actions beyond human audible judgment. Respiratory sounds of both healthy subjects and subjects who were infected with pulmonary tuberculosis were recorded from seven locations per lung on both the posterior and anterior chest walls, using self-designed hardware. Adaptive filtering signal and analysis techniques yielded a wide range of signal features. This included analysis for time, frequency and both wheeze and crackle adventitious respiratory sounds. Following the analysis, statistical methods identified the most attractive signal measurements capable of separating the recordings of healthy and unhealthy respiratory sounds. Selected signal features were used with neural network optimization to obtain a successful implementation for the semi-automated identification of healthy and unhealthy respiratory sounds originating from pulmonary tuberculosis, with a performance of over 80% for sensitivity, specificity and accuracy. The success of categorizing the recordings justifies the capabilities of the digital analysis of respiratory sounds and supports an argument for further research and refinement into the assessment of pulmonary tuberculosis by electronic auscultation. Further research is recommended, with improvements justified and highlighted in this report.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/1556 |
| Date | 03 1900 |
| Creators | Becker, Konrad Wilhelm |
| Contributors | Scheffer, C., Blanckenberg, M. M., University of Stellenbosch. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
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