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The paper presents an unsupervised approach to biomedical signal segmentation. The proposed segmentation process consists of several stages. In the first step, a state-space of the signal is reconstructed. In the next step, the dimension of the reconstructed state-space is reduced by projection into principal axes. The final step involves fuzzy clustering method. The clustering process is applied in the kernel-feature space. In the experimental part, the fetal heart rate (FHR) signal is used. The FHR baseline and the acceleration or deceleration patterns are the main signal nonstationarities but also the most clinically important signal features determined and interpreted in computer-aided analysis.
Rocznik
Tom
Strony
125--131
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
autor
- Silesian University of Technology, Institute of Electronics, Akademicka St. 16, 44-100 Gliwice, Poland
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autor
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Bibliografia
- [1] BEZDEK J.C., Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York, 1981.
- [2] DE CAMPOS D., BERNARDES J., Twenty-five years after the FIGO guidelines for the use of fetal monitoring: Time for a simplified approach?, Int. J. Gynecol. Obstet., 2010, Vol. 110, pp. 1–6.
- [3] FILIPPONE M., CAMASTRA F., MASULLI F., ROVETTA S., A survey of kernel spectral methods for clustering, 2008, Patt. Rec. 41, pp. 176–190.
- [4] FILIPPONE M., MASULLI F., ROVETTA S., Applying the possibilistic c–means algorithm in kernel induced spaces, IEEE Trans. Fuzzy Sys. 2010, 18, pp. 572–584.
- [5] GRAVES D., PEDRYCZ W., Kernel–based fuzzy clustering and fuzzy clustering. Comparative experimental study. Fuzzy Sets and Sys, 2010, 161, pp. 522–543.
- [6] KOTAS M., Projective filtering of time–aligned ECG beats, IEEE Trans. Biomed. Eng, 2004, 51, pp. 1129–1139.
- [7] MULLER K.R., MIKA S., RATSCH G., TSUDA K., SCHOLKOPF B., An introduction to kernel–based learning algorithms, IEEE Trans. Neural Net, 2001, 12, pp. 181–201.
- [8] PEDRYCZ W. Knowledge–based clustering, Wiley–Interscience, 2005.
- [9] PRZYBYLA T., PANDER T., HOROBA K., et al., A new Approach to Unsupervised Classification, Journal of Medical Informatics and Technologies, 2011, Vol. 17, pp. 105–111.
- [10] PRZYBYLA T., JEZEWSKI J., HOROBA K., et al., Hybrid Fuzzy Clustering Using LP Norms, in Intelligent Information and Database Systems, Eds. NGUYEN N., KIM C., JANIAK A., LNAI 6591/Lecture Notes in Computer Science, 2011, Springer Verlag, pp. 187–196.
- [11] PRZYBYLA T., ROJ D., JEZEWSKI J., et al., Generalized Fuzzy Clustering Metod, Journal of Medical Informatics and Technologies, 2010, Vol. 16, pp. 69–76.
- [12] SAYED–MOUCHAWEH M., MESSAI N., A clustering–based approach for the identification of a class of temporally switched linear systems, 2012, Patt. Rec. Lett. 33, pp. 144–151.
- [13] SCHOLKOPF B. SMOLA A., Learning with kernels, MIT Press 2002.
- [14] SCHREIBER T., KAPLAN D., Nonlinear noise reduction for electrocardiograms, 1996, Chaos 6, pp. 87–92.
- [15] SHAWE–TAYLOR J., CRISTIANINI N., Kernels methods for pattern analysis, Cambridge Univ. Press 2004.
- [16] SMALL M., Applied nonlinear time series analysis. Applications in physics, physiology and finance. World Scientific 2005.
- [17] TSAI D.M., LIN C.C., Fuzzy c–means based clustering for linearly and nonlinearly separable data, 2011, Patt. Rec. 44, pp. 1750–1760.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0026-0014