Browsing by Author "Botha, Gert Hendrik Renier"
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- ItemLung health diagnosis through cough sound analysis(Stellenbosch : Stellenbosch University, 2017-03) Botha, Gert Hendrik Renier; Niesler, T. R.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: This study investigates a simple and easily applied tool for TB screening based on the analysis of cough audio and objective clinical measurements. Tuberculosis is one of the most lethal diseases worldwide. There are various diagnosis methods for TB. However, in lower income areas, clinics lack funds to afford expensive equipment and employ the trained experts needed to interpret results. A database of cough audio recordings and clinical measurements was collected for this study. An automatic annotation system was developed using hidden Markov models (HMMs). The frame-accuracy of the annotation system is 87:16%. For audio based classification we considered logistic regression and Gaussian mixture models (GMMs). We found that filterbank energy features outperformed MFCC features when used for audio classification, which could indicate that cough audio contains information relevant to TB diagnosis that is not perceivable by the human auditory system. Feature selection was used to investigate the importance of different frequency bands for classification and, it was found that the optimal results were achieved when combining features from the human vowel range (below 1000Hz) with features from high frequency ranges. As the main metric of evaluation, we used the area under the receiver operator characteristic curve (AUC). This metric was chosen because it is not affected by class imbalance in the dataset. Our best reported AUC was 94:94%, with a standard deviation of 4:62%, which was obtained using a set of just 5 filterbank energies. We also showed that audio based classification obtains a higher AUC than classification on objective clinical measurements (meta data). Finally, we found that combining the audio and meta data classifier results using classifier fusion improved how well the model generalizes. By combining the best audio classifier with the best meta data classifier, we obtained a sensitivity, specificity, accuracy, AUC and kappa of 82:35%; 80:95%; 81:58%; 94:34% and 0:6867 respectively.