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Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The research presented in this thesis serves to provide a tool to autonomously screen for cardiovascular disease in the rural areas of Africa. Vital information thus obtained from patients can be communicated to advanced medical centres by Telemedicine. Cardiovascular disease is then detected in its initial stages, which is essential to its effective treatment. The system developed in this study uses recorded heart sounds and electrocardiogram signals to distinguish between normal and abnormal heart conditions. This system improves on standard diagnostic tools in that it does not require cumbersome and expensive imaging equipment or a highly trained operator. Heart sound- and electrocardiogram signals from 62 volunteers were recorded with the prototype Precordialcardiogram device as part of a clinical study to aid in the development of the autonomous auscultation software and to screen patients for cardiovascular disease. These volunteers consisted of 28 patients of Tygerberg Hospital with cardiovascular disease and, for control purposes, 34 persons with normal heart conditions. The autonomous auscultation system developed during this study, interprets data obtained with the Precordialcardiogram device to autonomously acquire a normal or abnormal diagnosis. The system employs wavelet soft thresholding to denoise the recorded signals, followed by the segmentation of heart sound by identifying peaks in the electrocardiogram. Novel frequency spectral information was extracted as features from the heart sounds, by means of ensemble empirical mode decomposition and auto regressive modelling. These features proved to be particularly significant and played a major role in the screening capability of the system. New time domain based features were identified, established on the specific characteristics of the various cardiovascular diseases encountered during the study. These features were extracted via the energy ratios between different parts of ventricular systole and diastole of each recorded cardiac cycle. The respective features were classified to characterise typical heart diseases as well as healthy hearts with an ensemble artificial neural network. Herein the decisions of all the members were combined to obtain a final diagnosis. The performance of the autonomous auscultation system used in concert with the Precordialcardiogram device prototype, as determined through the leave-one-out crossvalidation method, had a sensitivity rating of 82% and a specificity rating of 88%. These results demonstrate the potential benefit of the Precordialcardiogram device and the developed autonomous auscultation software in a Telemedicine environment. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die navorsing van 'n outonome toetsing en sifting stelsel vir kardiovaskulêre siektes in landelike dele van Afrika, vanwaar mediese inligting per telefoon versend kan word. Die apparaat maak vroeë opsporing van kardiovaskulêre siektes moontlik, wat essensieel is vir effektiewe behandeling daarvan en ook die koste-effek van hierdie siektes verminder. In die huidige ontwikkelde stelsel word normale sowel as abnormale hart-toestande getipeer met opnames van hartklanke sowel as elektrokardiogram-seine. Voordele wat hierdie stelsel bo standaard diagnostiese metodes het, sluit die hanteerbare formaat van die hele apparaat sowel as die nie-noodsaaklikheid van duur beeldskeppende apparaat, of hoogs opgeleide personeel. Hartklank- en elektrokardiogramseine van 62 vrywilligers is met die prototipe "Precordialcardiogram" apparaat opgeneem om by te dra tot die ontwikkeling van die rekenaar sagteware vir die outonome auscultatsie stelsel en om die pasiëntsiftingsvermoë daarvan te toets. Die vrywilligers het 28 pasiënte van Tygerberg hospitaal met abnormale harttoestande ingesluit, sowel as ‘n kontrolegroep van 34 persone met normale harttoestande. Die outonome auskultasie-stelsel wat tot stand gekom het deur hierdie ondersoek maak gebruik van “wavelet” sagte drempeling om geraas uit die opgeneemde seine te verwyder. Daarna word die hartklanke gesegmenteer deur die pieke van die elektrokardiogram te identifiseer. Deur middel van "ensemble empirical mode decomposition" en outoregressiewe modellering, is nuwe inligting aangaande die frekwensie spektra van hartklanke, aanwysend van spesifieke harttoestande, verkry. Die beduidendheid van hierdie eienskappe is bewys en het 'n belangrike rol in die siftingsvermoë van die stelsel gespeel. Hierbenewens is nuwe tyd-gebaseerde eienskappe van die onderskeie kardiovaskulêre siektes wat tydens die ondersoek bestudeer is, geïdentifiseer. Hierdie eienskappe is geëien deur die energie-verhoudings tussen verskillende dele van die ventrikulêre sistolie en diastolie van elke opgeneemde hartsiklus te ontleed. 'n "Ensemble artificial neural network" is gebruik om die geïdentifiseerde eienskappe van hartsiektes sowel as normale harttoestande, te klassifiseer. Hierin is besluite van al die lede van die netwerk gekombineer, ten einde ‘n finale diagnose te maak. Die klassifiseerder se geldigheid is kruis-bevestig deur middel van die laat-een-uit kruisbevestigings-metode. Deur middel van die kruis-bevestigingsmetode is die bedryfsvermoëns van die outonome auskultasie-stelsel, toegerus met die "Precordialcardiogram" apparaat, repektiewelik op 82% vir sensitiwiteit en 88% vir spesifisiteit vasgestel. Hierdie resultate demonstreer die benuttingspotensiaal van die apparaat in 'n Telemedisyne omgewing.