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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2414 |
| Date | 03 1900 |
| Creators | De Vos, Jacques Pinard |
| Contributors | Blanckenberg, M. M., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic 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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