The Auditory Steady State Response (ASSR) procedure has been established as a frequency specific, objective audiologic measure, which can provide reliable thresholds to within 10 dB of the behavioral thresholds. In order for ASSR to find its place in the existing framework of audiometric procedures, the full potential of the procedure needs to be explored. The aim of this study was to determine the accuracy of monotic ASSR in estimating hearing thresholds in a group of 15 normal hearing subjects and 15 hearing-impaired subjects. A comparative research design was implemented. Indicating that results obtained in the study was compared to relevant literature where dichotic multiple ASSR was implemented. This was done in order to ascertain ASSR’s capabilities with regard to stimulus presentation methods. Monotic single ASSR predicted behavioural thresholds in the normal hearing subjects within an average of 24 dB across the frequency range (0.5, 1, 2&4 kHz). In the hearing-impaired group, ASSR thresholds more closely resembled behavioural thresholds, with an average difference of 18 dB, which is consistent with recent literature. The literature suggests that better prediction of behavioural thresholds will occur with greater degrees of hearing loss, due to recruitment. The focus in this group also centered on the accurate prediction of the configuration of the hearing loss. It was found that ASSR could reasonably accurately predict the configuration of the hearing loss. In the last instance, monotic single and dichotic multiple ASSR were compared with regard to threshold estimation and prediction of configuration of the hearing loss in the hearing-impaired group. Little difference was reported between the two techniques with regard to the estimation of thresholds in both the normal hearing and hearing impaired groups. In conclusion it was established that monotic ASSR could predict behavioural thresholds of varying degrees and configurations of hearing loss in normal and hearing-impaired subjects with a reasonable amount of accuracy. At this stage, however, more research is required to establish the clinical validity of the procedure, before it is routinely included within an objective test battery. / Dissertation (M (Communication Pathology))--University of Pretoria, 2005. / Speech-Language Pathology and Audiology / Unrestricted

There has always been a need for objective tests that assess auditory function in infants, young children, and/or any patient whose development level precludes the use of behavioral audiometric techniques. Although the Auditory Brainstem Response (ABR) is seen as the ‘gold standard’ in the field of objective audiometry, it presents with its own set of limitations. The Auditory Steady State Response (ASSR) has gained considerable attention and is seen as a promising addition to the AEP ‘family’ to address some of the limitations of the ABR. The ASSR promises to estimate all categories of hearing loss (mild to profound) in a frequency specific manner. It also indicates to the possibility to validate hearing aid fittings by determining functional gain of hearing aids by determining unaided and aided ASSR thresholds. An exploratory research design was selected in order to compare unaided thresholds, obtained through the use of three different procedures – ABR, ASSR and behavioral thresholds. Aided thresholds were also obtained and compared with two procedures – the aided ASSR (measured and predicted) and aided behavioral threshold. The results indicated that both the ABR (tone burst and click) and ASSR provided a reasonable estimation of the subsequently obtained behavioral audiograms. The ASSR, however, approximated the behavioral thresholds closer than the ABR and were furthermore able to quantify hearing thresholds accurately for subjects with severe and profound hearing losses. The result indicated further that the ASSR can be instrumental in the validation process of hearing aid fittings in infants. These results demonstrated however, that the ASSR measured thresholds underestimate the aided behavioral thresholds and the aided ASSR predicted thresholds overestimate the aided behavioral thresholds. The research concluded that the ASSR is useful in estimating frequency-specific behavioral thresholds accurately in infants and validating hearing aid fittings. Until evidence is sufficient to recommend the ASSR as primary electrophysiological measure of hearing in infants, the ASSR should be used in conjunction with the ABR – following a test battery approach in the diagnostic process of hearing loss in infants. The ASSR further shows great promise in validating hearing aid fittings, but this specific application of the ASSR needs further research evidence on large groups to validate the procedure. / Dissertation (Master of Communication Pathology)--University of Pretoria, 2007. / Speech-Language Pathology and Audiology / unrestricted

Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone. / Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.