Unsupervised feature selection in cardiac arrhythmias analysis

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Volumn , Issue , 2009, Pages 2571-2574

  Rodriguez Sotelo J L ^a   Cuesta Frau, D ^b   Peluffo Ordonez D ^a   Castellanos Dominguez G ^a  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

^b UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; COST FUNCTIONS; DISEASES;

ALGORITHM PERFORMANCE; AUTOMATIC SELECTION; CARDIAC ARRHYTHMIA; CLASSIFICATION ALGORITHM; COMMON FEATURES; DATA PARTITION; DISSIMILARITY MEASURES; UNSUPERVISED FEATURE SELECTION;

FEATURE EXTRACTION;

EID: 77951002898     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5335284     Document Type: Conference Paper

References (18)

1. AAMI. Recomended Practice for Testing and Reporting Performance Results of Ventricular Arrhythmia Detection Algorithms. ANSI/AAMI EC57:1998/(R)2003. Association for the Advancement of Medical Instrumentation, Arlington, VA 22201-25762, 1999.
2. C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press Inc., New York, 1995.
3. R. Ceylan and Y. Özbay. Comparison of fcm, pca and wt techniques for classification ecg arrhythmias using artificial neural network. Expert Systems with Applications, 33(2):286-295, 2007.
4. D. Cuesta, M. Biagetti, R. Quinteiro, P. Mico-Tormos, and M. Aboy. Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching. Medical and Biological Engineering and Computing, 45(3):229-239, 2007.
5. D. Cvetkovic, E. D. Übeyli, and I. Cosic. Wavelet transform feature extraction from human ppg, ecg, and eeg signal responses to elf pemf exposures: A pilot study. Digital Signal Processing, 18(5):861-874, 2008.
6. P. De-Chazal, M. O'Dwyer, and R. Reilly. Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transaction on biomedical engineering, 51(7):1196-1206, 2004.
7. P. Hansen and N. Mladenovic. J-means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34:405-413, 2001.
8. N. Hughes, S. Roberts, and L. Tarassenko. Semi-supervised learning of probabilistic models for ecg segmentation. volume 1, pages 434-437, Sept. 2004.
9. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sörnmo. Clustering ecg complexes using hermite functions and self-organising maps. IEEE trans. on. Biomed., 48:838-847, 2000.
10. G. Moody and M. R. The impact of the mit-bih arrhythmia database. IEEE Transactions on engineering, medicine and biology, 15(1):34-50, 1985.
11. S. Osowski, L. Hoai, and T. Markiewicz. Support vector machine based expert system for reliable heart beat recognition. IEEE trans. on. Biomed., 61:1-7, 2002.
12. M. Paoletti and C. Marchesi. Discovering dangerous patterns in longterm ambulatory ecg recordings using a fast qrs detection algorithm and explorative data analysis. Computer Methods and programs in biomedicine, 82:20-30, 2006.
13. J. Rodríguez-Sotelo, D. Cuesta-Frau, and G. Castellanos- Domínguez. Unsupervised classification of atrial heartbeats using a prematurity index and wave morphology features. Medical and Biological Engineering and Computing, 47(7):731-741, 2009.
14. J. Sahambi, S. Tandon, and R. P. Bhatt. Using wavelet transform for ecg charecterization. an on-line digital signal processing system. Engineering in Medicine and Biology Magazine, IEEE, 16:77-83, january 1997.
15. R. Silipo. Investigating electrocardiographic features in fuzzy models for cardiac arrhythmia classification. In IDAMAP-99. Intelligent data analysis in medicine and pharmacology, Washington, dc, October 1999.
16. M. S. Thaler. The only EKG book you'll ever need. Lippincott Williams & Wilkins, 5th edition, September 2006.
17. L. Wolf and A. Shashua. Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach. Journal of Machine Learning Research, 6:1855-1887, 2005.
18. A. Ziarani and A. Konrad. A nonlinear adaptive method of elimination of power line interference in ecg signals. IEEE transactions on biomedical engineering, 49(6):540-547, 2002.