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Öğe Feature Extraction Using Time-Frequency Analysis for Monophonic-Polyphonic Wheeze Discrimination(IEEE, 2015) Ulukaya, Sezer; Sen, Ipek; Kahya, Yasemin P.The aim of this study is monophonic-polyphonic wheeze episode discrimination rather than the conventional wheeze (versus non-wheeze) episode detection. We used two different methods for feature extraction to discriminate monophonic and polyphonic wheeze episodes. One of the methods is based on frequency analysis and the other is based on time analysis. Frequency analysis based method uses ratios of quartile frequencies to exploit the difference in the power spectrum. Time analysis based method uses mean crossing irregularity to exploit the difference in periodicity in the time domain. Both methods are applied on the data before and after an image processing based preprocessing step. Calculated features are used in classification both individually and in combinations. Support vector machine, k-nearest neighbor and Naive Bayesian classifiers are adopted in leave-one-out scheme. A total of 121 monophonic and 110 polyphonic wheeze episodes are used in the experiments, where the best classification performances are 71.45% for time domain based features, 68.43% for frequency domain based features, and 75.78% for a combination of selected best features.Öğe A Lung Sound Classification System based on the Rational Dilation Wavelet Transform(IEEE, 2016) Ulukaya, Sezer; Serbes, Gorkem; Sen, Ipek; Kahya, Yasemin P.I n this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.Öğe A Novel Method for Determination of Wheeze Type(IEEE, 2015) Ulukaya, Sezer; Sen, Ipek; Kahya, Yasemin P.Among respiratory disorders, obstructive diseases such as asthma and chronic obstructive pulmonary disease (COPD) constitute an important group. To our knowledge, there does not exist a study in the literature that quantifies the relationship between the type of wheeze and the type or severity of the disease. This study, aims at classifying wheeze type rather than classical normal-wheeze sound classification studies in the literature. In this study, we propose a method based on Multiple Signal Classification (MUSIC) algorithm to differentiate between monophonic and polyphonic wheezes, without a need for pre-training the algorithm. The algorithm determines the true labels of monophonic and polyphonic wheezes with 100% and 78% accuracy, respectively. Since there does not exist a method in the literature that has been proposed specifically for this problem, only the results of the most relevant few studies have been presented. Since the proposed system can directly estimate the frequency, we consider the method proposed here would be a useful quantification method for further studies in medical literature, on finding correlations between wheezes and disorders.