Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

Artificial Intelligence in Medicine

Volumn 28, Issue 3, 2003, Pages 231-263

  Carrault, G ^a   Cordier, M O ^b   Quiniou, R ^b   Wang, F ^a  

^a UNIVERSITÉ DE RENNES 1   (France)

^b CAMPUS DE BEAULIEU   (France)

Author keywords

Artificial neural network; Cardiac arrhythmia classification; Chronicle recognition; ECG; Inductive logic programming; Medical data analysis; Temporal abstraction

Indexed keywords

CARDIOLOGY; DATABASE SYSTEMS; LEARNING SYSTEMS; LOGIC PROGRAMMING; NEURAL NETWORKS;

RELATIONAL LEARNING;

ELECTROCARDIOGRAPHY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA ANALYSIS; ELECTRIC ACTIVITY; ELECTROCARDIOGRAM; HEART ARRHYTHMIA; HEART DISEASE; MEDICAL RECORD; P WAVE; PRIORITY JOURNAL; QRS COMPLEX; RECOGNITION;

EID: 0041663535     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(03)00066-6     Document Type: Article

References (64)

1. Afonso V.X., Tompkins W.J., Nguyen T.Q., Luo S.L., ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46:1999;192-202.
2. Al Nashash H.A., Cardiac arrhythmia classification using neural networks. Technol. Health Care. 46:1998;1417-1425.
3. Bardou A., Auger P., Birkui P., Chasse J.-L., Modeling of cardiac electrophysiological mechanisms: from action potential genesis to its propagation in myocardium. Crit. Rev. Biomed. Eng. 24:1996;141-221.
4. Behlouli H. Apprentissage auto-améliorant et modélisation de la dynamique temporelle de données évolutives par réseaux de neurones. Application au diagnostic et à la prédiction en électrocardiologie quantitative. Ph.D. thesis, INSA de Lyon, France; 1998.
5. Bourlas P, Giakoumakis E, Papakonstantinou G. A knowledge acquisition and management system for ECG diagnosis. In: Magoulas G, Prentza A, editors. Proceedings of the Workshop on Machine Learning in Medical Applications, Chania, Greece; 1999. p. 30-6.
6. Bratko I, Mozetič I, Lavrač N. Kardio: a study in deep and qualitative knowledge for expert systems. Cambridge, USA: MIT Press; 1989.
7. Carrault G, Cordier M-O, Quiniou R, Garreau M, Bellanger J-J, Bardou A. A model-based approach for learning to identify cardiac arrhythmias. In: Horn W, Shahar Y, Lindberg G, Andreassen S, Wyatt J, editors. Proceedings of AIMDM-99: Artificial Intelligence in Medicine and Medical Decision Making, vol. 1620 of LNAI. Aalborg, Denmark: Springer-Verlag; 1999. p. 165-74.
8. Clocksin WF, Mellish CS. Programming in prolog. Berlin: Springer-Verlag; 1981.
9. Coast D.A., Stern R.M., Cano G.C., Briller S.A., An approach to cardiac arrhythmia analysis using Hidden Markov Models. IEEE Trans. Biomed. Eng. 27(9):1990;826-836.
10. Coiera E. Designing for decision support in a clinical monitoring environment. In: Schizas CN, Chistodoulides G, editors. Proceedings of the International Conference on Medical Physics and Biomedical Engineering, Nicosia, Cyprus; 1994. p. 130-42.
11. Dechter R., Meiri I., Pearl J., Temporal constraint networks. Artif. Intell. 49(1-3):1991;61-95.
12. Dousson C., Alarm driven supervision for telecommunication networks. II. On-line chronicle recognition. Ann. Telecommun. 51(9-10):1996;501-508.
13. Dousson C, Gaborit P, Ghallab M. Situation recognition: representation and algorithms. In: Proceedings of IJCAI-93. Chambéry, France: Morgan Kaufman; 1993. p. 166-72.
14. Einthoven W., Un nouveau galvanomètre. Arch. Néerland. Sci. Exact. Nat. 6:1901;625-633.
15. Fokapu O, Girard J-P. A new approach for P wave detection using analytic signal. In: Proceedings of the 15th IEEE/EMBS Conference. San Diego, USA: IEEE Computer Society Press; 1993. p. 400-1.
16. Freeman SP. P wave detection of ambulatory ECG. In: Proceedings of the 13th IEEE/EMBS Conference. Orlando, USA: IEEE Computer Society Press; 1991. p. 647-8.
17. Gritzali F., Towards a generalized scheme for QRS detection in ECG waveforms. Signal Process. 15:1988;183-192.
18. Gritzali F., Frangakis G., Papakonstantinou G., Detection of the P and T waves in an ECG. Comput. Biomed. Res. 22:1989;83-91.
19. Guertin M. Abductive inference of events: diagnosing cardiac arrhythmias. In: Proceedings of the Florida Artificial Intelligence Research Symposium, Key West, USA; 1996.
20. Gustafson D., Willsky A., Wang J., Lancaster M., Triebwasser J., ECG/VCG rhythm diagnosis using statistical signal analysis, parts 1 and 2. IEEE Trans. Biomed. Eng. 25:1978;344-361.
21. Ham F., Han S., Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans. Biomed. Eng. 43(4):1996;425-430.
22. Hernández A., Carrault G., Mora F., Improvement of a P wave detector by a bivariate classification stage. Trans. Inst. Meas. Contr. 22(3):2000;231-242.
23. Hernández A., Carrault G., Mora F., Bardou A., Overview of CARMEN: a new dynamic quantitative cardiac model for ECG monitoring and its adaptation to observed signals. Acta Biotheor. 48(3-4):2000;303-322.
24. Hernández A.I., Carrault G., Mora F., Passariello G., Schleich J.M., Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring. IEEE Trans. Biomed. Eng. 46:1999;1186-1190.
25. Hérault J, Jutten C. Réseaux neuronaux et traitement du signal. Paris, France: Éditions Hermès, Traité des nouvelles technologies; 1994.
26. Jenkins J., Caswell S., Detection algorithms in implantable cardioverter defibrillator. Proc. IEEE. 84(3):1996;428-445.
27. Kókai G, Alexin Z, Gyimóthy T. Analysing and learning ECG waveforms. In: Muggleton S, editor. Proceedings of ILP'96, vol. 1314 of LNAI. Stockholm, Sweden: Springer-Verlag; 1996. p. 127-45.
28. Koulouris A., Papakonstantinou G., Tsanakas P., A decentralised multichannel length transformation algorithm and its parallel implementation for real-time ECG monitoring. Comput. Biomed. Res. 33:2000;227-244.
29. Kristiansen DE, Husøy JH, Eftestøl T. Rhythm detection in ECG signals. In: Proceedings of the Norwegian Signal Processing Symposium, Norsig, Stavanger, Norway; 1995. p. 119-24.
30. Kundu M., Nasipuri M., Basu D., A knowledge-based approach to ECG interpretation using fuzzy logic. IEEE Trans. Syst. Man Cybernet. 28(2):1998;237-243.
31. van Laer W, de Raedt L, Dzeroski S. On multi-class problems and discretization in inductive logic programming. In: Ras ZW, Skowron A, editors. Proceedings of ISMIS-97: 10th International Symposium on Methodologies for Intelligent Systems, vol. 1325 of LNAI. Charlotte, NC: Springer-Verlag; 1997. p. 277-86.
32. Lavrač N. Machine learning for data mining in medicine. In: Horn W, Shahar Y, Lindberg G, Andreassen S, Wyatt J, editors. Proceedings of AIMDM'99, vol. 1620 of LNAI. Aalborg, Denmark: Springer-Verlag; 1999. p. 47-62.
33. Lavrač N, Dzeroski S, Grobelnik M. Learning non-recursive definitions of relations with LINUS. In: Kodratoff Y, editor. Proceedings of EWSL, Porto, Portugal; 1991. p. 265-81.
34. Lavrač N, Dzeroski S, Pirnat V, Krizman V. Learning rules for early diagnosis of rheumatic diseases. In: Mayoh B, editor. Proceedings of the third Scandinavian Conference on AI, vol. 12 of Frontiers in Artificial Intelligence and Applications. Roskilde, Denmark: IOS Press; 1991. p. 138-49.
35. Le Blanc R., Quantitative analysis of cardiac arrhythmias. Crit. Rev. Biomed. Eng. 14(1):1986;1-43.
36. Long W.J., Temporal reasoning for diagnosis in a causal probabilistic knowledge base. Artif. Intell. Med. 8:1996;193-215.
37. Lucas P., Tholen A., van Oort G., A model-based system for pacemaker reprogramming. Artif. Intell. Med. 17:1999;249-269.
38. Moody GB, Mark RG. The MIT-BIH arrhythmia database on CD-ROM and software for use with it. In: Proceedings of Computers in Cardiology Conference, vol. 17. San Diego, USA: IEEE Computer Society Press; 1990. p. 185-8.
39. Muggleton S., Inductive logic programming: issues, results and the challenge of learning language in logic. Artif. Intell. 114(1-2):1999;283-296.
40. Nugent C.D., Webb J.A.C., Black N.D., Wright G.T.H., McIntyre M., An intelligent framework for the classification of the 12-lead ECG. Artif. Intell. Med. 8:2000;363-372.
41. Pan J., Tompkins W.J., A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32:1985;230-236.
42. Passariello G., Mora F., Carrault G., Le Pichon J.-P., Intelligent patient monitoring and management systems: a review. IEEE Eng. Med. Biol. 12(4):1993;23-33.
43. Quiniou R, Cordier M-O, Carrault G, Wang F. Application of ILP to cardiac arrhythmia characterisation for chronicle recognition. In: Rouveirol C, Sebag M, editors. Proceedings of ILP-2001, vol. 2157 of LNAI. Strasbourg, France: Springer-Verlag; 2001. p. 193-215.
44. Quinlan J. Machine learning and ID3. Los Altos: Morgan Kauffman; 1992.
45. de Raedt L, van Laer W. Inductive constraint logic. In: Jantke KP, Shinohara T, Zeugmann T, editors. Proceedings of ALT-95: 6th Workshop on Algorithmic Learning Theory, vol. 997 of LNAI. Fukuoka, Japan: Springer-Verlag; 1995. p. 80-94.
46. Reddy S, Elko P, Chistenson D, Rowlandson G. Detection of P waves in resting ECG: a preliminary study. In: Proceedings of Computers in Cardiology Conference. San Diego, USA: IEEE Computer Society Press; 1992. p. 87-90.
47. Ryan T.J., Antman E.M., Brooks N.H., 1999 update: ACC/AHA guidelines for the management of patients with acute myocardial infarction. J. Am. College Cardiol. 34:1999;890-911.
48. Senhadji L., Carrault G., Bellanger J.-J., Comparing wavelet transforms for recognizing cardiac patterns. IEEE Eng. Med. Biol. Mag. 14(2):1995;167-173.
49. Senhadji L, Wang F, Hernández AI, Carrault G. Wavelets extrema representation for QRS-T cancellation and P wave detection. In: Proceedings of Computers in Cardiology Conference. Memphis, USA: IEEE Computer Society Press; 2002.
50. Shapiro EY. Algorithmic program debugging, ACM distinguished dissertation. Cambridge, MA: MIT Press; 1982.
51. Shibahara T. On using causal knowledge to recognise vital signals: knowledge-based interpretation of arrhythmia. In: Joshi AK, editor. Proceedings of IJCAI-85. Los Angeles, CA: Morgan Kaufmann; 1985. p. 307-14.
52. Silipo R. Investigating electrocardiographic features in fuzzy models for cardiac arrhythmia classification. In: Proceedings of IDAMAP-99, Washington, DC; 1999. p. 123-34.
53. Silipo R., Marchesi C., Artificial neural networks for automatic ECG analysis. IEEE Trans. Signal Process. 46(5):1998;1417-1425.
54. Siregar P., Chahine M., Lemoulec F., LeBeux P., An interactive qualitative model in cardiology. Comput. Biomed. Res. 28(6):1995;443-478.
55. Srinivasan A., Muggleton S., King R., Sternberg M., Theories for mutagenicity: a study of first-order and feature-based induction. Artif. Intell. J. 85(1-2):1996;277-299.
56. Thakor N., Zhu Y., Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38:1991;785-794.
57. Tong D.A., Widman L.E., Model-based interpretation of the ECG: a methodology for temporal and spatial reasoning. Comput. Biomed. Res. 26(3):1993;206-219.
58. Tsien CL, Fackler JC. An annotated data collection system to support intelligent analysis of intensive care unit data. In: Liu X, Cohen P, Berthold M, editors. Advances in intelligent data analysis. London: Springer-Verlag; 1997. p. 111-21.
59. Uckun S., Intelligent systems in patient monitoring and therapy management. A survey of research projects. Int. J. Clin. Monit. Comput. 11:1994;241-253.
60. Wang F, Quiniou R, Carrault G, Cordier M-O. Learning structural knowledge from the ECG. In: Crespo J, Maojo V, Martin F, editors. Proceedings of ISMDA-2001, vol. 1933 of LNCS. Madrid, Spain: Springer-Verlag; 2001. p. 288-94.
61. Watrous RL, Towell G. A patient-adaptive neural network ECG patient monitoring algorithm. In: Proceedings of Computers in Cardiology Conference. Wien, Austria: IEEE Computer Society Press; 1995. p. 229-32.
62. Wieben O., Afonso V.X., Tompkins W.J., Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system. Med. Biol. Eng. Comput. 37:1999;560-565.
63. World Health Organization. The world health report 1999: making a difference. Geneva, Switzerland: World Health Organization; 1999.
64. Zong W, Jiang D. Automated ECG rhythm analysis using fuzzy reasoning. In: Proceedings of Computers in Cardiology Conference. Cleveland, USA: IEEE Computer Society Press; 1998. p. 69-72.