EEG transient event detection and classification using association rules

IEEE Transactions on Information Technology in Biomedicine

Volumn 10, Issue 3, 2006, Pages 451-457

  Exarchos, Themis P ^a   Tzallas, Alexandros T ^a   Fotiadis, Dimitrios I ^a,b   Konitsiotis, Spiros ^c   Giannopoulos, Sotirios ^c  

^a UNIVERSITY OF IOANNINA   (Greece)

^b INSTITUTE OF COMPUTER SCIENCE   (Greece)

^c UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Association rules; Clustering; Electroencephalographic (EEG); Epilepsy; Spike detection; Transient events

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DATA RECORDING; FEATURE EXTRACTION;

ASSOCIATION RULES; CLUSTERING; EPILEPSY; SPIKE DETECTION; TRANSIENT EVENTS;

ELECTROENCEPHALOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; CLASSIFICATION; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMAN; INFORMATION RETRIEVAL; METHODOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; CLUSTER ANALYSIS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MODELS, NEUROLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TIME FACTORS;

EID: 33746905061     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2006.872067     Document Type: Article

References (45)

1. L. Tarassenko, Y. U. Khan, and M. R. G. Holt, "Identification of interictal spikes in the EEG using neural network analysis," Proc. Inst. Elect. Eng. - Sci. Meas. Technol., vol. 145, pp. 270-278, 1998.
2. C. C. C. Pang, A. R. M. Upton, G. Shine, and M. V. Kamath, "A comparison of algorithms for detection of spikes in the electroencephalogram," IEEE Trans. Biomed. Eng., vol. 50, no. 4, pp. 521-525, Apr. 2003.
3. B. Liu, W. Hsu, and Y. Ma, "Integrating classification and association rule mining," in Proc. 4th Int. Conf. Knowledge Discovery and Data Mining (KDD-98), New York, 1998, pp. 80-86.
4. W. Li, J. Han, and J. Pei, "CMAR: Accurate and efficient classification based on multiple class-association rules," in Proc. 2001 IEEE Int. Conf. Data Mining, San Jose, CA, 2001, pp. 369-376.
5. W. R. Webber, B. Litt, K. Wilson, and R. P. Lesser, "Practical detection 9of epileptiform discharges (EDs) in the EEG using an artificial neural network: A comparison of raw and parameterized EEG data," Electroen. Clin. Neurophysiol., vol. 91, pp. 194-204, 1994.
6. E. Niedermeyer and F. L. da Silva, Electroencephalography, Basic Principles, Clinical Applications and Related Fields. Philadelphia, PA: Williams & Wilkins, 1993.
7. W. Hosteler, H. Doller, and W. Homan, "Assessment of a computer program to detect epileptiform spikes," Electroencephalogr. Clin. Neurophysiol., vol. 83, pp. 1-11, 1992.
8. F. H. L. da Silva, "Detection of nonstationarities in EEGs using the autoregressive model-an application to EEGs of epileptics," in CEAN: Computerized EEG Analysis, G. Dolce and H. Kunkel, Eds. Stuttgart, Germany: Fischer, 1975, pp. 180-199.
9. T. Kalayci and O. Ozdamar, "Wavelet pre-processing for automated neural network detection of EEG spikes," IEEE Eng. Med. Biol. Mag., vol. 14, no. 2, pp. 160-166, Mar./Apr. 1995.
10. P. Gloor, "Contributions of electroencephalography and electrocorticography in the neurosurgical treatment of the epilepsies," Adv. Neurol., vol. 8, pp. 59-105, 1975.
11. H. S. Park, Y. H. Lee, N. G. Kim, D. S. Lee, and S. I. Kim, "Detection of epileptiform activities in the EEG using neural network and expert system," Medinfo, vol. 9, pp. 1255-1259, 1998.
12. C. J. James, R. D. Jones, P. J. Bones, and G. J. Carroll, "Detection of epileptiform discharges in the EEG by a hybrid system compromising mimetic, self-organized artificial neural network, and fuzzy logic stages," Clin. Neurophysiol., vol. 110, pp. 2049-2063, 1999.
13. N. Acir, I. Oztura, M. Kuntalp, B. Balkan, and C. Guzelis, "Automatic detection of epilepticform events in EEG by a three stage procedure based on artificial neural networks," IEEE Trans. Biomed. Eng., vol. 52, no. 1, pp. 30-40, Jan. 2005.
14. P. Valenti, E. Cazamajou, M. Scarpettini, A. Aizemberg, W. Silva, and S. Kochen, "Automatic detection of spikes using data mining models," J. Neurosci. Meth., to be published.
15. M. Adjouadi, M. Cabrerizo, M. Ayala, D. Sanchez, I. Yaylali, P. Jayakar, and A. Barreto, "Detection of interictal spikes and artefactual data through orthogonal transformations," J. Clinical Neuroph., vol. 22, pp. 53-64, 2005.
16. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th Int. Conf. Very Large Data Bases, Santiago, Chile, 1994, pp. 487-499.
17. J. Bourien, J. J. Bellanger, F. Bartolomei, P. Chauvel, and F. Wendling, "Mining reproducible activation patterns in epileptic intracerebral EEG signals: Application to interictal activity," IEEE Trans. Biomed. Eng., vol. 51, no. 2, pp. 304-315, Feb. 2004.
18. S. Konias and N. Maglaveras, "A rule discovery algorithm appropriate for ECG signals," in Proc. 31st Comput. Cardiology, Chicago, IL, 2004, pp. 57-60.
19. A. Flexer, "Data mining and electroencephalography," Stat. Methods Med. Res., vol. 9, pp. 395-413, 2000.
20. D. Sommer and M. Golz, "Clustering EEG-segments using hierarchical agglomerative methods and self-organising maps," in Proc. Annu. Conf. Eur. Neural Network Society, Vienna, Austria, 2001, pp. 642-649.
21. P. Wahlberg and G. Salomonsson, "Feature extraction and clustering of EEG epileptic spikes," Comput. Biomed. Res., vol. 29, pp. 382-394, 1996.
22. P. Wahlberg and G. Lantz, "Methods for robust clustering of epileptic EEG spikes," IEEE Trans. Biomed. Eng., vol. 47, no. 7, pp. 857-876, Jul. 2000.
23. A. T. Tzallas, C. D. Katsis, P. S. Karvelis, D. I. Fotiadis, S. Konitsiotis, and S. Giannopoulos, "Classification of transient events in EEG recordings," in Proc. 2nd Int. Conf. Adv. Biomed. Signal Inf. Process., Malta, 2004, pp. 14-20.
24. C. Castellaro, G. Favaro, A. Castellaro, A. Casagrande, S. Castellaro, D. V. Puthenparampil, and C. F. Salimbeni, "An artificial intelligence approach to classify and analyse EEG traces," Clin. Neurophysiol., vol. 32, pp. 193-214, 2002.
25. R. Kothari and D. Pitts, "On finding the number of clusters," Pattern Recognit. Lett., vol. 20, pp. 405-416, 1999.
26. A. A. Dingle, R. D. Jones, G. J. Carroll, and W. R. Fright, "A multistage system to detect epileptiform activity in the EEG," IEEE Trans. Biomed. Eng., vol. 40, no. 12, pp. 1260-1268, Dec. 1993.
27. U. M. Fayyad and K. B. Irani, "Multi-interval discretization of continuous-valued attributes for classification learning," in Proc. 13th Int. Joint Conf. Artif. Intell., 1993, pp. 1022-1027.
28. R. Kohavi and M. Sahami, "Error-based and entropy-based discretization of continuous features," in Proc. 2nd Int. Conf. Knowledge Data Mining, Menlo Park, CA, 1996, pp. 114-119.
29. H. Liu and H. Motoda, Feature Extraction, Construction and Selection: A Data Mining Perspective. Boston, MA: Kluwer, 1998.
30. M. A. Hall and G. Holmes, "Benchmarking attribute selection techniques for discrete class data mining," IEEE Trans. Knowl. Data Eng., vol. 15, no. 6, pp. 1437-1447, Nov./Dec. 2003.
31. R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artif. Intell., vol. 97, pp. 273-324, 1997.
32. I. Guyon and A. Elissee, "An introduction to variable and feature selection," J. Mach. Learning Res., vol. 3, pp. 1157-1182, 2003.
33. H. Liu and R. Setiono, "A probabilistic approach to feature selection - a filter solution," in Proc. 13th Int. Conf. Machine Learning, Bari, Italy, 1996, pp. 319-327.
34. J. Kittler, "Feature set search algorithms," in Pattern Regognition and Signal Processing, C. H. Chen, Ed. Alphen aan den Rijn, The Netherlands: Sijthoff and Noordhoff, 1978
35. W. T. Blume and J. F. Lemieux, "Morphology of spikes in spike-and-wave complexes," Electroencephalogr. Clin. Neurophysiol., vol. 69, pp. 508-515, 1988.
36. M. Elton, M. Scherg, and D. Von Cramon, "Effects of high-pass filter frequency and slope on BAEP amplitude, latency and wave form," Electroencephalogr. Clin. Neurophysiol, vol. 57, pp. 490-494, 1984.
37. J. R. Quinlan, C4.5: Programs for Machine Learning. Sam Mateo, CA: Morgan Kaufmann, 1993.
38. X. Yin and J. Han, "CPAR: Classification based on predictive association rules," in Proc. 3rd SIAM Int. Conf. Data Mining, San Francisco, CA, 2003, pp. 331-335.
39. W. W. Cohen, "Fast effective rule induction," in Proc. ICML-95, Tarragona, Spain, 1995, pp. 115-123.
40. P. Clark and R. Boswell, "Rule induction with CN2: Some recent improvements," in Proc. Machine Learning-EWSL-91, Berlin, Germany, 1991, pp. 151-163.
41. M. Kantardzic, Data Mining: Concepts, Models, Methods and Algorithms. Hoboken, NJ: Wiley-IEEE Press, 2002.
42. H. H. Jasper, "The ten twenty electrode system of the international federation," Clin. Neurophysiol., vol. 10, pp. 371-375, 1958.
43. A. Saastamoinen, T. Pietilä, A. Värri, M. Lehtokangas, and J. Saarinen, "Waveform detection with RBF network - Application to automated EEG analysis," Neurocomputing, vol. 20, pp. 1-13, 1998.
44. V. Schetinin and J. Schult, "The combined technique for detection of artefacts in clinical electroencephalograms of sleeping newborns," in IEEE Trans. Inform. Technol. Biomed., vol. 8, no. 1, pp. 28-35, Mar. 2004.
45. A. Kusiak, J. A. Kern, K. H. Kernstine, and B. T. L. Tseng, "Autonomous decision-making: A data mining approach," IEEE Trans. Inform. Technol. Biomed., vol. 4, no. 4, pp. 274-284, Dec. 2000.