Premature ventricular beat detection by using spectral clustering methods

Computers in Cardiology

Volumn 34, Issue , 2007, Pages 149-152

  Ribeiro, B R ^a,c   Marques, A M ^a   Henriques, J H ^a   Antunes, Ma ^b  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b CENTRO HOSPITALAR E UNIVERSITÁRIO DE COIMBRA   (Portugal)

^c UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BASIC SHAPES; CLASSIFICATION METHODS; ECG SIGNALS; EIGEN DECOMPOSITIONS; FEATURE DIMENSIONS; INPUT FEATURES; PREMATURE VENTRICULAR BEATS; RANKING CRITERIONS; RECURSIVE FEATURE ELIMINATIONS; SPECTRAL CLUSTERING; SPECTRAL CLUSTERING METHODS; SPECTRAL PROPERTIES; SVM CLASSIFIERS; SVM-RFE; WAVE AMPLITUDES;

CARDIOLOGY; CLUSTER ANALYSIS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 62949199062     PISSN: 02766574     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIC.2007.4745443     Document Type: Conference Paper

References (10)

1. Ge D, Srinivasan N, Krishnan SM. Cardiac arrhythmia classification using autoregressive modeling. Biomed Eng Online 2002;1-5.
2. Christov I, Jekova I, Bortolan G. Premature ventricular contraction classification by the kth nearest-neighbours rule. Physiol Meas 2005;26:123-130.
3. Ramirez-Rodriguez CA, Hernandez-Silveira MA. Multithread implementation of a fuzzy neural network for automatic ECG arrhythmia detection. Comp Cardiol 2001; 28:297-300.
4. Guyon I, Weston J, S SB, V.Vapnik. Gene selection for cancer classification using SVM. Machine Learning 2002; (46):389-422.
5. Kohavi R, John G. Wrappers for feature subset selection. Artificial Intelligence 1997;273-324.
6. J. JS, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis 2000;22(8):888-905.
7. Vapnik VN. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
8. Ribeiro B, MArques A, Henriques J, Antunes M. Choosing real time predictors for ventricular arrhythmia detection. International Journal of Pattern Recognition and Artificial Intelligence (to appear);.
9. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. Technical report, Computer Science and Information Engineering Dep, National Taiwan University, 2000.
10. Witten IH, Frank E. Data Mining Practical Machine Learning Tools and Techniques. Elsevier, 2005.