Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

Browse

Start date: 2006End date: 2009Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal processingAuthor: search.filters.author.Aydın, Nizamettin

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    PublicationMetadata only
    Kaleidomap visualizations of cardiovascular function in critical care medicine
    (2006) Bale, Kim; Chapman, Paul; Purdy, Jon H.; Aydın, Nizamettin; Dark, Paul Michael; Bale, Kim, Department of Computer Science, University of Hull, Hull, United Kingdom; Chapman, Paul, Department of Computer Science, University of Hull, Hull, United Kingdom; Purdy, Jon H., Department of Computer Science, University of Hull, Hull, United Kingdom; Aydın, Nizamettin, Department of Genetics and Bioinformatics, Bahçeşehir Üniversitesi, Istanbul, Turkey; Dark, Paul Michael, Intensive Care Research Group, The University of Manchester, Manchester, United Kingdom
    In this paper we consider how the use of Kaleidomaps can facilitate our understanding and interpretation of large complex multivariate medical datasets relating to cardiovascular function in critical care medicine. Kaleidomaps are a new technique for the visualization of multivariate time-series data. They build upon the classic cascade plot and use the curvature of a line to enhance the detection of periodic patterns within multivariate dual-periodicity datasets. Kaleidomaps keep user interaction to a minimum, facilitating the rapid identification of periodic patterns not only within their own variants but also across many different sets of the variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide medical experts with a set of visualization tools that permit the combination of medical datasets in their raw form and also with the results of mathematical analysis. © 2006 IEEE. © 2008 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    Data mining usage in emboli detection
    (2007) Karahoca, Adem; Kucur, Turkalp; Aydın, Nizamettin; Karahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kucur, Turkalp, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Asymptomatic circulating cerebral emboli, which are particles bigger than blood cells, can be detected by transcranial Doppler ultrasound. In certain conditions asymptomatic embolic signals (ES) appear to be markers of increased stroke risk. ES, reflected by an embolus, have usually larger amplitude than the signals from normal blood flow and show a transient characteristic. A number of methods to detect cerebral emboli have been studied in the literature. In this study, data mining techniques have been used in order to increase sensitivity and specificity of an embolic signal detection system. The classification results of different methods have been compared by using a data set including 100 ES, 100 speckle and 100 artifact. The ROC analysis results show that adaptive neuro fuzzy inference (ANFIS) system method appears to give better results. © 2007 IEEE. © 2008 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    DWT based adaptive threshold determination in embolic signal detection
    (2007) Aydın, Nizamettin; Aydın, Nizamettin, Engineering Faculty, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Early and accurate detection of emboli is important for monitoring of preventive therapy in stroke-prone patients. An embolic signal caused by emboli can be detected by Doppler ultrasound. Embolic signals are highly nonstationary and last only for a short time. Advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells and artifacts. Any detection method involving change detection must rely on determination of an appropriate threshold. In this paper, an adaptive threshold determination method based on discrete wavelet transform (DWT) and statistical properties of the data under investigation is described. The results reveal that the proposed adaptive threshold results in a robust embolic signal detection system. © 2007 IEEE. © 2008 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    A novel information-theoretic clustering algorithm for robust, unsupervised classification
    (2007) Temel, Turgay; Aydın, Nizamettin; Temel, Turgay, Department of Electrical Engineering, Fatih Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Engineering Faculty, Bahçeşehir Üniversitesi, Istanbul, Turkey
    A new information-theoretic, unsupervised, subtractive clustering algorithm is proposed. The algorithm eliminates threshold constraint to detect possible cluster members. Cluster centers are formed with minimum entropy. Instead of using a fixed-threshold, a decision region is formed with the use of maximum mutual information. Cluster members are chosen with a relative-cost assigned in partitions of data set. The algorithm yields more reliably distributed cluster numbers in statistical sense, hence reducing further computation for validation, which is justified for a set of synthetic data. ©2007 IEEE. © 2008 Elsevier B.V., All rights reserved.