Visual data mining with self-organising maps for ventricular fibrillation analysis

Computer Methods and Programs in Biomedicine

Volumn 111, Issue 2, 2013, Pages 269-279

  Rosado Muñoz, Alfredo ^a   Martínez Martínez, José M ^a   Escandell Montero, Pablo ^a   Soria Olivas, Emilio ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Data visualization; Heart disease; Self organising maps; Ventricular fibrillation

Indexed keywords

CLINICAL PROBLEMS; CONTINUOUS SURFACE; HEART DISEASE; TIME FREQUENCY DOMAIN; TIME-FREQUENCY DISTRIBUTIONS; VENTRICULAR FIBRILLATION; VENTRICULAR TACHYCARDIA; VISUAL DATA MINING;

DATA VISUALIZATION; PATIENT REHABILITATION; SELF ORGANIZING MAPS;

PATIENT TREATMENT;

ALGORITHM; ARTICLE; DATA BASE; DATA MINING; ELECTROCARDIOGRAPHY; HEART RATE; HEART RHYTHM; HEART VENTRICLE FIBRILLATION; HEART VENTRICLE TACHYCARDIA; INFORMATION PROCESSING; NERVE CELL; SELF ORGANIZING MAP; SINUS RHYTHM; SPECTROMETRY; STATISTICAL ANALYSIS; VISUAL INFORMATION;

DATA VISUALIZATION; HEART DISEASE; SELF-ORGANISING MAPS; VENTRICULAR FIBRILLATION;

ALGORITHMS; DATA MINING; DATABASES, FACTUAL; ELECTROCARDIOGRAPHY; HEART RATE; HUMANS; NEURAL NETWORKS (COMPUTER); SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; TACHYCARDIA, VENTRICULAR; TIME FACTORS; VENTRICULAR FIBRILLATION;

EID: 84880045515     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2013.02.011     Document Type: Article

References (48)

1. Moe G.K., Abildskov J.A., Han J. Factors responsible for the initiation and maintenance of ventricular fibrillation. Sudden Cardiac Death 1964, Grune and Stratton, New York. B. Surawicz, E. Pellegrino (Eds.).
2. Beck C.S., Pritchard W.H., Giles W., Mensah G. Ventricular fibrillation of long duration abolished by electric shock. Journal of the American Medical Association 1947, 135:985-986.
3. Yakaitis R.W., Ewy G.A., Otto C.W. Infuence of time and therapy on ventricular fibrillation in dogs. Critical Care Medicine 1980, 8(3):157-163.
4. Capucci A., Aschieri D., Piepoli M., Rosi A., Arvedi M., Villani G. Early defibrillation through first responders triples survivals rates from out of hospital cardiac arrest in an Italian community. European Heart Journal 2001, 22:242.
5. White R., Asplin B., Bugliosi T., Hankins D. High discharge survival rate after out-of-hospital ventricular fibrillation with rapid defibrillation by police and paramedics. Annals of Emergency Medicine 1996, 28(5):480-485.
6. Faddy S.C. Reconfirmation algorithms should be standard of care in automated external defibrillators. Resuscitation 2006, 68(3):409-415.
7. Chen S., NV N.V.T., Mower M.M. Ventricular fibrillation detection by a regression test on the autocorrelation function. Medical & Biological Engineering & Computing 1987, 25(3):241-249.
8. Clayton R.H., Murray A., Campbell R.W. Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG. Medical & Biological Engineering & Computing 1993, 31(2):111-117.
9. Chen S.W., Clarkson P.M., Fan Q. A robust sequential detection algorithm for cardiac arrhythmia classification. IEEE Transactions on Biomedical Engineering 1996, 43(11):1120-1125.
10. Zhang X.S., Zhu Y.S., Thakor N.V., Wang Z.Z. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Transactions on Biomedical Engineering 1999, 46(5):548-555.
11. Davidenko J.M., Pertsov A.V., Salomonsz R., Baxter W., Jalife J. Stationary and drifting spiral waves of excitation in isolated cardiac muscle. Nature 1992, 355(6358):349-351.
12. Clayton R.H., Murray A., Campbell R.W. Evidence for electrical organization during ventricular fibrillation in the human heart. Journal of Cardiovascular Electrophysiology 1995, 6(8):616-624.
13. Jalife J., Gray R.A., Morley G.E., Davidenko J.M. Evidence for electrical organization during ventricular fibrillation in the human heart. Chaos 1998, 8(1):79-93.
14. Herschleb J.N., Heethaar R.M., de Tweel I.V., Zimmerman A.N.E., Meijler F.L. Signal analysis of ventricular fibrillation. IEEE Computers in Cardiology 1979, 49-54.
15. Murray A., Campbell R.W.F., Julian D.G. Characteristics of the ventricular fibrillation waveform. IEEE Computers in Cardiology 1985, 275-278.
16. Nygards M.E., Hulting J. Recognition of ventricular fibrillation utilizing the power spectrum of the ECG. IEEE Computers in Cardiology 1978, 393-397.
17. Barro S., Ruiz R., Cabello D., Mira J. Algorithmic sequential decision making in the frequency domain for life threatening centricular arrhythmias and aimitative artifacts: a diagnostic system. Journal of Biomedical Engineering 1989, 11(4):320-328.
18. Nolle F.M., Bowser R.W., Badura F.K., Catlett J.M., Gudapati R.R., Hee T.T., Moos A.N., Sketch M.H. Evalutation of frequency-domain algorithm to detect ventricular fibrillation in the surface electrocardiogram. IEEE Computers in Cardiology 1988, 337-340.
19. Afonso V.X., Tompkins W.J. Detecting ventricular fibrillation. IEEE Engineering in Medicine & Biology 1995, 14(2):152-159.
20. Clayton R.H., Murray A. Comparison of techniques for time-frequency analysis of the ECG during human ventricular fibrillation. IEEE Proceedings on Science and Measurements Technology 1998, 145:301-306.
21. Rosado A., Serrano A., Martínez M., Soria E., Calpe J., Bataller M. Detailed study of time-frequency parameters for ventricular fibrillation detection. ESEM Europen Society for Engineering and Medicine 1999, 379-380.
22. Amann A., Tratnig R., Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Transactions on Biomedical Engineering 2007, 54(1):174-177.
23. Bai B., Wang Y. Ventricular fibrillation detection based on empirical mode decomposition. Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on 2011, 1-4.
24. Li Y., Bisera J., Weil M., Tang W. An algorithm used for ventricular fibrillation detection without interrupting chest compression. IEEE Transactions on Biomedical Engineering 2012, 59(1):78-86.
25. Zhang C., Zhao J., Tian J., Li F., Jia H. Support vector machine for arrhythmia discrimination with tci feature selection. Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on 2011, 111-115.
26. A. Amann, R. Tratnig, K. Unterkofler, Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators, BioMedical Engineering Online 4 (60) (2005 October).
27. Clayton R.H., Murray A., Campbell R.W. Recognition of ventricular fibrillation using neural networks. Medical & Biological Engineering & Computing 1994, 32(2):217-220.
28. Neurauter A., Eftestol T., Kramer-Johansen J., Abella B.S., Sunde K., Wenzel V., Lindner K.H., Eilevstjonn J., Myklebust H., Steen P.A., Strohmenger H.-U. Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks. Resuscitation 2007, 73(2):253-263.
29. Pardey J. Detection of ventricular fibrillation by sequential hypothesis testing of binary sequences. IEEE Computers in Cardiology 2007, 573-576.
30. Jekova I., Mitev P. Detection of ventricular fibrillation and tachycardia from the surface ecg by a set of parameters acquired from four methods. Physiological Measurement 2002 November, 23(4):629-634.
31. Übeyli E.D. Usage of eigenvector methods in implementation of automated diagnostic systems for ecg beats. Digital Signal Processing 2008, 18(1):33-48.
32. Rosado-Mu noz A., Camps-Valls G., Guerrero-Martínez J., Francés-Villora J.V., Mu noz-Marí J., Serrano-López A.J. Enhancing feature extraction for VF detection using data mining techniques. IEEE Computers in Cardiology 2002, 29:209-212. 10.1109/CIC.2002.1166744.
33. Kohonen T. Self-organization and associative memory 1989, Springer-Verlag New York, Inc, New York, NY, USA. third ed.
34. Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics 1982, 43:59-69.
35. Kohonen T. Self-Organizing Maps 2000, Springer Berlin Heidelberg. third ed.
36. Haykin S. Neural Networks and Learning Machines. 2009, Prentice Hall. third ed.
37. Visual Explorations in Finance. 1998, Springer-Verlag New York, Inc, Secaucus, NJ, USA. first ed. G.J. Deboeck, T.K. Kohonen (Eds.).
38. Alakhdar Y., Martínez-Martínez J.M., Guimerà-Tomás J., Escandell- Montero P., Benítez J., Soria-Olivas E. Visual data mining in physiotherapy using self-organazing maps: a new approximation to the data analysis. Medical Application of Intelligent Data Analysis: Research Advancements 2012, 186-193. IGI Global, Ch. 12. R. Magdalena, E. Soria, J. Guerrero, J. Gómez (Eds.).
39. Kohonen T., Oja E., Simula O., Visa A., Kangas J. Engineering applications of the self-organizing map. Proceedings of the IEEE 1996, 4(10):1358-1384.
40. Díaz I., Cuadrado A.A., Diez A.B., Domínguez M., Fuertes J.J., Prada M.A. Supervision of industrial processes using self organizing maps. Intelligent Data Analysis for Real-Life Applications: Theory and Practice. 2012, 206-227. IGI Global, Ch. 11. R. Magdalena-Benedito, M. Martínez-Sober, J.M. Martínez-Martínez, P. Escandell-Montero, J. Vila-Francés (Eds.).
41. Soria E., Martín J.D., Fernández C., Magdalena R., Serrano A.J., Moreno Á. Qualitative modelling of time series using self-organizing maps: application to animal science. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, ACS'06, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point 2006, 183-187.
42. Fernández C., Soria E., Martín-Guerrero J.D., Serrano A.J. Neural networks for animal science applications: two case studies. Expert Systems with Applications 2006, 31(2):444-450.
43. Magdalena R., Fernández C., Martín J.D., Soria E., Martínez M., Navarro M.J., Mata C. Qualitative analysis of goat and sheep production data using self-organizing maps. Expert Systems 2009, 26(2):191-201.
44. Vesanto J., Alhoniemi E., Himberg J., Kiviluoto K., Parviainen J. Self-organizing map for data mining in matlab: the som toolbox. Simulation News Europe (25) 1999, 54.
45. Cohen L. Time Frequency Analysis. 1995, Prentice Hall PTR, New Jersey, USA. first ed.
46. Atienza F.A., Rojo-Álvarez J.L., Camps-Valls G., Mu noz A.R., García-Alberola A. Bootstrap feature selection in support vector machines for ventricular fibrillation detection. ESANN 2006, 233-238.
47. Alonso-Atienza F., Rojo-Álvarez J.L., Rosado-Mu noz A., Vinagre J.J., GarcA-a-Alberola A., Camps-Valls G. Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection. Expert Systems with Applications 2012, 39(2):1956-1967.
48. Kiviluoto K. Topology preservation in self-organizing maps. IEEE International Conference on Neural Networks, 1996, vol. 1 1996, 294-299.