Examining cross-database global training to evaluate five different methods for ventricular beat classification

Physiological Measurement

Volumn 30, Issue 7, 2009, Pages 661-677

  Chudacek V ^a   Georgoulas, G ^b   Lhotska L ^a   Stylios, C ^b   Petrik M ^a   Cepek M ^a  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b Epirus Institute of Technology Arta   (Greece)

Author keywords

Classification; ECG; Fuzzy rule based tree; Holter; Neural networks; Premature ventricular complex; Rule based tree; Self organizing map; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; CONFORMAL MAPPING; DATABASE SYSTEMS; DECISION TREES; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES; TEMPLATE MATCHING;

BEAT CLASSIFICATION; FUZZY RULE BASED; FUZZY RULE-BASED TREE; HOLTER; HOLTER RECORDINGS; NEURAL-NETWORKS; PREMATURE VENTRICULAR COMPLEX; RULE BASED; RULE-BASED TREE; SUPPORT VECTORS MACHINE;

ELECTROCARDIOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL EVALUATION; CLUSTER ANALYSIS; COMPARATIVE STUDY; DATA BASE; DECISION TREE; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; HEART BEAT; HEART VENTRICLE; HOLTER MONITORING; MARKET; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; TRAINING; CLASSIFICATION; ELECTROCARDIOGRAPHY; HEART ARRHYTHMIA; HUMAN; METHODOLOGY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARRHYTHMIAS, CARDIAC; DATABASES AS TOPIC; ELECTROCARDIOGRAPHY, AMBULATORY; HEART VENTRICLES; HUMANS; SENSITIVITY AND SPECIFICITY;

EID: 70249093684     PISSN: 09673334     EISSN: 13616579     Source Type: Journal    
DOI: 10.1088/0967-3334/30/7/010     Document Type: Article

References (52)

1. AAMI-Association for the Advancement of Medical Instrumentation 1998 Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms 1998
2. Acar B, Savelieva I, Hemingway H and Malik M 2000 Automatic ectopic beat elimination in short-term heart rate variability measurement Comput. Methods Programs Biomed. 63 123-31
3. AHA-DB 1997 AHA database series 1 American Heart Association
4. Bakardijan H 1992 Ventricular beat classifier using fractal number clustering Med. Biol. Eng. Comput. 30 495-502
5. Bortolan G, Jekova I and Christov I 2005 Comparison of four methods for premature ventricular contraction and normal beat clustering Comput. Cardiol. 32 921-4
6. Buhmann M D and Ablowitz M J 2003 Radial Basis Functions: Theory and Implementations (Cambridge: Cambridge University Press)
7. Burges C J C 1998 A tutorial on support vector machines for pattern recognition Data Mining and Knowledge Discovery 2 121-67
8. Carrault G, Cordier M O, Quiniou R and Wang F 2003 Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms Artif. Intell. Med. 28 231-63
9. Castro A, Bveda C and Arcay B 2007 A new method for the initialization of clustering algorithms based on histogram analysis Visualization, Imaging, and Image Processing 2007 (Palma de Mallorca: Acta Press)
10. Ceylan R, zbay Y and Karlik B 2009 A novel approach for classification of ECG arrhythmias: type-2 fuzzy clustering neural network Expert Syst. Appl. 36 6721-6
11. Chazal P D, O'Dwyer O and Reilly R B 2004 Automatic classification of heartbeats using ECG morphology and heartbeat interval features IEEE Trans. Biomed. Eng. 51 1196-206
12. Chiu C, Lin T and Liau B 2005 Using correlation coefficient in ECG waveform for arrhythmia detection Biomed. Eng. Appl. Basis Commun. 17 147-52
13. Christov I I 2004 Real time electrocardiogram QRS detection using combined adaptive threshold Biomed. Eng. Online 3 28
14. Christov I and Bortolan G 2004 Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks Physiol. Meas. 25 1281-90
15. Christov I, Gomez-Herrero G, Krasteva V, Jekova I, Gotchev A and Egiazarian K 2006 Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification Med. Eng. Phys. 28 876-87
16. Christov I, Jekova I and Bortolan G 2005 Premature ventricular contraction classification by the Kth nearest-neighbours rule Physiol. Meas. 26 123-30
17. Chudek V, Lhotsk L, Stylios C, Georgoulas G and Stavia M 2006 Comparison of methods for premature ventricular beat detection Int. Special Topics ITAB Conf. (Piscataway, NJ: IEEE)
18. Cuesta-Frau D, Biagetti M, Mico-Tormos R Q and Aboy M 2007 Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching Med. Biol. Eng. Comput. 45 229-39
19. Cuesta-Frau D, Perez-Cortes J C and Andreu-Garcia G 2001 Clustering of electrocardiograph signals in computer-aided Holter analysis Comput. Methods Programs Biomed. 72 179-96
20. Daskalov I K, Dotsinsky I A and Christov I I 1998 Developments in ECG acquisition, preprocessing, parameter measurement, and recording IEEE Eng. Med. Biol. Mag. 17 50-8
21. Dokur Z and Olmez T 2001 ECG beat classification by a novel hybrid neural network Comput. Methods Programs Biomed. 66 167-81
22. Dotsinsky I A and Stoyanov T V 2004 Ventricular beat detection in single channel electrocardiograms Biomed. Eng. Online 3 3
23. Fridericia L S 1920 Die Systolendauer im Elektrokardiogramm bei normalen Menschen und bei erzkranken Acta Medica Scand. Stockh. 57 469-86
24. Goldberger A L, Amaral L, Glass L, Hausdorf J M, Ivanov P C, Mark R G, Mietus J E, Moody G B, Peng C K and Stanley H E 2000 PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals Circulation 101 e215-20
25. Haykin S 1999 Neural Networks: A Comprehensive Foundation (Englewood Cliffs, NJ: Prentice-Hall)
26. Holter N J 1961 New method for heart studies Science 134 1214-20
27. Hosseini G, Luo D and Reynolds K J 2006 The comparison of different feed forward neural network architectures for ECG signal diagnosis Med. Eng. Phys. 28 372-8
28. Hu Y H, Palreddy S and Tompkins W J 1997 A patient-adaptable ECG beat classifier using a mixture of experts approach IEEE Trans. Biomed. Eng. 44 891-900
29. Jager F 2002 Feature extraction and shape representation of ambulatory electrocardiogram using the Karhunen-Loeve transform Elektroteh. Vestn. 69 83-89
30. Jekova I, Bortolan G and Christov I 2008 Assessment and comparison of different methods for heartbeat classification Med. Eng. Phys. 30 248-57
31. Klir G J and Yuan B 1995 Fuzzy Sets and Fuzzy Logic: Theory and Applications (Englewood Cliffs, NJ: Prentice-Hall)
32. Kohonen T 1995 Self-Organizing Maps (Berlin: Springer)
33. Kyriacou E et al 2003 Multi-purpose health care telemedicine systems with mobile communication link support BioMedical Engineering OnLine 2 7
34. Lagerholm M, Peterson C, Braccini G, Edenbrandt L and Sornmo L 2000 Clustering ECG complexes using Hermite functions and self-organizing maps IEEE Trans. Biomed. Eng. 47 838-48
35. Macfarlane P W, van Oosterom A, Pahlm O, Kligfield P, Janse M and Camm J 1989 Comprehensive Electrocardiology (Oxford: Pergamon)
36. Madala H and Ivakhnenko A 1994 Inductive Learning Algorithm for Complex System Modelling (Boca Raton, FL: CRC Press)
37. Mark R and Moody G 1997 MIT-BIH Arrhythmia Database (Online) http://ecg.mit.edu/dbinfo.html
38. Minhas F A and Arif M 2008 Robust electrocardiogram (ECG) beat classification using discrete wavelet transform Physiol. Meas. 29 555-70
39. Moody G and Mark R 1989 QRS morphology representation and noise estimation using the Karhunen-Loeve transform Comput. Cardiol. 16 269-72
40. Moreas J C, Seixas M O, Vilani F N and Costa E V 2002 A real time QRS complex classification method using Mahalanobis distance Comput. Cardiol. 29 201-4
41. Muller K R, Mika S, Ratsch G, Tsuda K and Scholkopf B 2001 An introduction to Kernel-based learning algorithms IEEE Trans. Neural Netw. 2 181-201
42. Nguyen H T and Walker E A 1997 A First Course in Fuzzy Logic (Boca Raton, FL: CRC Press)
43. Olmez T, Dokur Z and Yazgan E 1997 Classification of ECG waveform using genetic algorithms Proc. 19th Int. Conf. IEEE/EMBS
44. Papouchado M, Cox H, Bailey J, White W and Spreadbury T 2001 Early experience with transmission of data from moving ambulances to improve the care of patients with myocardial infarction J. Telemed. Telecare 7 27-8
45. Rumelhart D E, Hinton G E and Williams R J 1986 Learning representations by back-propagating errors Nature 323 533-6
46. Tsipouras M G, Fotiadis D I and Sideris D 2002 Arrhythmia classification using the RR-interval duration signal Comput. Cardiol. 29 485-8
47. Tsipouras M G, Voglis C and Fotiadis D I 2007 A framework for fuzzy expert system creation-application to cardiovascular diseases IEEE Trans. Biomed. Eng. 54 11
48. Tsipouras M G, Voglis C, Lagaris I E and Fotiadis D I 2005 Cardiag arrhytmia classification using support vector machines 3rd European Medical and Biological Engineering Conf.
49. Vesanto J, Himberg J, Alhoniemi E and Parhankangas J 2000 Self-organizing map in Matlab: the SOM Toolbox, TOOLMET
50. Witten I and Eibe F 2005 Data Mining: Practical Machine Learning Tools and Techniques 2nd edn (San Francisco, CA: Morgan Kaufmann)
51. Yu S N and Chou K 2009 Selection of significant independent components for ECG beat classification Expert Syst. Appl. 36 2088-96
52. Zadeh L A 1965 Fuzzy sets Inf. Control 8 338-53