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Start date: 2010End date: 2012Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal processingAuthor: search.filters.author.Aydın, Nizamettin

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    Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform
    (2010) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, Turkey
    Early and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB). © 2010 IEEE. © 2011 Elsevier B.V., All rights reserved.
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    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, Turkey
    Pulmonary 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.