Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
Browse
3 results
Search Results
Publication Metadata only Effect of different window and wavelet types on the performance of a novel crackle detection algorithm(2011) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Department of Electrical Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection, which uses time- frequency and time-scale analysis, and the performance comparison for different window types in time-frequency analysis and also for different wavelet types in time-scale analysis is presented. In the proposed method, various feature sets are extracted using time-frequency and time-scale analysis for different windows and wavelet types. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data for different windows and wavelets are proposed. © 2011 Springer-Verlag. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection(2011) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Department of Electrical Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed. © 2011 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Metadata only Pulmonary crackle detection using time-frequency analysis, Zaman-frekans anali̇zi̇ kullanarak pulmoner çitirti tespi̇ti̇(2012) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Bilgisayar Mühendisliǧi Bölümü, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristics. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency analysis. In order to understand the effect of using different window types in time-frequency analysis in detecting crackles, various types of windows are used such as Gaussian, Blackman, Hanning, Hamming, Bartlett, Triangular and Rectangular. The extracted features both individually and as an ensemble of networks sets are fed into k-Nearest Neighbor classifier. Besides, in order to improve the success of the classifier, prior to the time frequency analysis, frequency bands containing no-crackle information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy compared to the conventional discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets, which are extracted using different window types, for pre-processed and non pre-processed data with k-Nearest Neighbor are extensively evaluated and compared. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.