Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

Computer Methods and Programs in Biomedicine

Volumn 88, Issue 3, 2007, Pages 217-233

  Dubois, Rémi ^a   Maison Blanche, Pierre ^b   Quenet, Brigitte ^a   Dreyfus, Gérard ^a  

^a CNRS   (France)

^b HÔPITAL LARIBOISIÈRE   (France)

Author keywords

Adaptive signal processing; Cardiac wave recognition; ECG; Machine learning; Neural network; Orthogonal forward regression

Indexed keywords

BIOELECTRIC POTENTIALS; DATABASE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

ADAPTIVE SIGNAL PROCESSING; CARDIAC WAVE RECOGNITION; MACHINE-LEARNING ALGORITHMS; ORTHOGONAL FORWARD REGRESSION;

ELECTROCARDIOGRAPHY;

ALGORITHM; ARTICLE; AUTOMATION; CLASSIFICATION; DATA BASE; ELECTROCARDIOGRAM; HEART BEAT; HUMAN; LEARNING; MACHINE; NERVE CELL NETWORK; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; P WAVE; PARAMETER; PROBABILITY; Q WAVE; R WAVE; SIGNAL DETECTION; SIGNAL PROCESSING; ST SEGMENT; T WAVE; VALIDATION PROCESS;

ALGORITHMS; AUTOMATION; ELECTROCARDIOGRAPHY; MODELS, THEORETICAL; NONLINEAR DYNAMICS; PROBABILITY; SENSITIVITY AND SPECIFICITY;

EID: 36148957864     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2007.09.005     Document Type: Article

References (46)

1. Wagner G.S., and Marriott H.J.L. Marriott's Practical Electrocardiography. 10th ed. (2001), Williams & Wilkins, Philadelphia: Lippincott
2. Crawford M.H., Bernstein S.J., Deedwania P.C., DiMarco J.P., Ferrick K.J., Garson Jr. A., Green L.A., Greene H.L., Silka M.J., Stone P.H., Tracy C.M., Gibbons R.J., Alpert J.S., Eagle K.A., Gardner T.J., Gregoratos G., Russell R.O., Ryan T.J., and Smith Jr. S.C. ACC/AHA guidelines for ambulatory electrocardiography: executive summary and recommendations. A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to revise the guidelines for ambulatory electrocardiography). Circulation 100 (1999) 886-893
3. Kowey P.R., and Kocovic D.Z. Cardiology patient pages. Ambulatory electrocardiographic recording. Circulation 108 (2003) 31-33
4. Laguna P., Moody G.B., Garcia J., Goldberger A.L., and Mark R.G. Analysis of the ST-T complex of the electrocardiogram using the Karhunen-Loeve transform: adaptive monitoring and alternans detection. Med Biol Eng Comput 37 (1999) 175-189
5. Soria-Olivas E., Martinez-Sober M., Calpe-Maravilla J., Guerrero-Martinez J.F., Chorro-Gasco J., and Espi-Lopez J. Application of adaptive signal processing for determining the limits of P and T waves in an ECG. IEEE Trans Biomed Eng 45 (1998) 1077-1080
6. Sun Y., Chan K.L., and Krishnan S.M. Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc Disord 5 28 (2005)
7. Gritzali F., Frangakis G., and Papakonstantinou G. Detection of the P and T waves in an ECG. Comput Biomed Res 22 (1989) 83-91
8. Vila J.A., Gang Y., Rodriguez Presedo J.M., Fernandez-Delgado M., Barro S., and Malik M. A new approach for TU complex characterization. IEEE Trans Biomed Eng 47 (2000) 764-772
9. Sternickel K. Automatic pattern recognition in ECG time series. Comput Methods Programs Biomed 68 (2002) 109-115
10. Laguna P., Jane R., and Caminal P. Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res 27 (1994) 45-60
11. Li C., Zheng C., and Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng 42 (1995) 21-28
12. Martinez J., Almeida R., Olmos S., Rocha A., and Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 51 (2004) 570-581
13. Suppappola S., Sun Y., and Chiaramida S.A. Gaussian pulse decomposition: an intuitive model of electrocardiogram waveforms. Ann. Biomed. Eng. 25 (1997) 252-260
14. Simon B.P., and Eswaran C. An ECG classifier designed using modified decision based neural networks. Comput. Biomed. Res. 30 (1997) 257-272
15. Osowski S., and Linh T. ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48 (2001) 1265-1271
16. Koski A. Modelling ECG signals with Hidden Markov Models. Artif. Intell. Med. 8 (1996) 453-471
17. Thoraval L., Carrault G., and Bellanger J.J. Heart signal recognition by Hidden Markov Models: the ECG case. Methods Inform. Med. 33 (1994) 10-14
18. Senhadji L., Thoraval L., and Carrault G. Continuous wavelet transform: ECG recognition based on phase and modulus representations and hidden Markov model. Wavelet Appl. Med. Biol. (1996) 439-463
19. Jankowski S., Tijink J., Vumbaca G., Marco B., and Karpinski G. Morphological Analysis of ECG Holter Recordings by Support Vector Machines. Proceedings of the Third International Symposium on Medical Data Analysis. Springer-Verlag (2002)
20. Poon K., Okin P.M., and Kligfield P. Diagnostic performance of a computer-based ECG rhythm algorithm. J. Electrocardiol. 38 (2005) 235-238
21. Salerno S.M., Alguire P.C., and Waxman H.S. Training and competency evaluation for interpretation of 12-lead electrocardiograms: recommendations from the American College of Physicians. Ann. Intern. Med. 138 (2003) 747-750
22. AHA-DB, AHA Database Series 1: The American Heart Association Electrocardiographic - ECRI, 1997.
23. MIT-DB, The MIT-BIH Arrhythmia database (third ed.): Massachusetts Institute of Technology - http://www.ecg.mit.edu, 1997.
24. Pan J., and Tompkins W.J. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32 (1985) 230-236
25. J. Stephenson, Detection of isoelectric baseline and high frequency noise within electrocardiographique signal, ESPCI, Laboratoire d'Electronique -http://www.neurones.espci.fr/Francais.Docs/STEPHENSON.htm 2000.
26. Dubois R. Application des nouvelles méthodes d'apprentissage à la détection précoce d'anomalie en électrocardiographie. Laboratoire d'Electronique (2003), ESPCI, Paris-France
27. Dubois R., Quenet B., Faisandier Y., and Dreyfus G. Building meaningful representations for nonlinear modeling of 1d- and 2d-signals: applications to biomedical signals. Neurocomputing 69 (2006) 2180-2192
28. Broyden C.G. The Convergence Double Rank Minimization Algorithms. 2. The New Algorithm. J. Inst. Maths Appl. 6 (1970) 222-231
29. Metz C.E. Basic principles of Roc analysis. Semin. Nucl. Med. 8 (1978) 283-298
30. Xue Q., and Reddy S. Algorithms for computerized QT analysis. J. Electrocardiol. 30 (1998) 181-186
31. Couderc J.P., Zareba W., Moss A.J., Sarapa N., Morganroth J., and Darpo B. Identification of sotalol-induced changes in repolarization with T wave area-based repolarization duration parameters. J. Electrocardiol. 36 Suppl. (2003) 115-120
32. Holter N. New method for heart studies. Science 134 (1961) 1214-1229
33. MacFarlane P., and Veitsch L. Comprehensive Electrocardiology: Theory and Pratice in Health and disease (1989), Pergamon Press Inc, New York
34. Hurst J.W. Ventricular Electrocadiography (1990), Williams & Milkins Publishers, Lippincot
35. Laguna P., Mark R., Goldberger A., and Moody G. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol. (1997)
36. Coast D.A., Cano G.G., and Briller S.A. Use of hidden Markov model for electrocardiographic signal analysis. J. Electrocardiol. 23 Suppl. (1991) 184-191
37. The CSE Working Party. Recommendations for measurement standards in quantitative electrocardiography. Eur. Heart J. 6 (1985) 815-825
38. Schreier G., Hayn D., and Lobodzinski S. Development of a new QT algorithm with heterogenous ECG databases. J. Electrocardiol. 36 (2003) 145-150
39. Lux R. Principal components analysis: an old but powerful toll for ECG analysis. Int. J. Bioelectromagnetism 5 (2003) 342-345
40. Leanderson S., Laguna P., and Sörnmo L. Estimation of the respiratory frequency using spatial information in the VCG. Med. Eng. Phys. 25 (2003) 501-507
41. Berg J., Fayn J., Edenbrandt L., Lundh B., Malmstrom P., and Rubel P. CAVIAR: a tool to improve serial analysis of the 12-lead electrocardiogram. Clin. Physiol. 15 (1995) 435-445
42. Vialatte F., Chichocki G., Dreyfus G., Musha T., Rutkowski M., and Gervais R. Blind source separation and sparse bump modeling of time-frequency representation of EEG signals: new tools for early detection of Alzheimer's disease. Machine Learning for Signal Processing (MLSP 2005) (2005)
43. Vialatte F.B., Martin C., Dubois R., Haddad J., Quenet B., Gervais R., and Dreyfus G. A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics. Neural Networks 20 (2007) 194-209
44. Bishop C. Neural Networks for Pattern Recognition (1995), Clarendon Press, Oxford
45. Dreyfus G. Neural Networks Methodology and Applications (2005), Springer
46. Cristianini N., and Shawe-Taylor J. An Intoduction to Support Vector Machines (2000), University Press, Cambridge