Adaptive AR and neurofuzzy approaches: Access to cerebral particle signatures

IEEE Transactions on Information Technology in Biomedicine

Volumn 10, Issue 3, 2006, Pages 559-566

  Kouamé, Denis ^a   Biard, Mathieu ^a,b   Girault, Jean Marc ^a   Bleuzen, Aurore ^b  

^a UNIVERSITÉ DE TOURS   (France)

^b Centre d'Innovations Technologiques   (France)

Author keywords

Alarm; Autoregressive (AR); Detection; Doppler; False; Model; Neurofuzzy; Nondetection; Score

Indexed keywords

MATHEMATICAL MODELS; MEDICAL IMAGING; REGRESSION ANALYSIS; ULTRASONIC IMAGING;

AUTOREGRESSIVE (AR) MODELING; CEREBRAL ARTERY; CEREBRAL EMBOLISM; CEREBRAL VASCULAR DISORDER; NEUROFUZZY;

BRAIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN EMBOLISM; COMPUTER ASSISTED DIAGNOSIS; DOPPLER ECHOGRAPHY; ECHOGRAPHY; FUZZY LOGIC; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; INTRACRANIAL EMBOLISM; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY, DOPPLER, TRANSCRANIAL;

EID: 33746905456     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2005.862463     Document Type: Article

References (28)

1. M. Spencer, G. Thomas, S. Nicholls, and L. Sauvage, "Detection of middle cerebral artery emboli during carotid endarterectomy using transcranial doppler ultrasonography," Stroke, vol. 21, pp. 415-423, 1990.
2. L. Cohen, "Time-frequency distributions, a review," Proc. IEEE, vol. 77, no. 7, pp. 941-981, Jul. 1989.
3. M. Moehring and J. Keppler, "Pulse doppler ultrasound detection characterization and size estimation of emboli in flowing blood," IEEE Trans. Biomed. Eng., vol. 41, no. 1, pp. 35-44, Jan. 1994.
4. M. Moehring and J. Ritcey, "Sizing emboli using pulse doppler ultrasound, Part I: Verification of ebr model," IEEE Trans. Biomed. Eng., vol. 43, no. 6, pp. 572-580, Jun. 1996.
5. E. V. V. Zuilen, W. H. Mess, C. Jansen, I. V. D. Tweel, J. V. Gijn, and R. G. Ackerstaff, "Automatic embolus detection compared with human experts: A doppler ultrasound study," Stroke, vol. 27, pp. 1840-1843, 1996.
6. R. Brucher and D. Russell, "Automatic embolus detection and artifact rejection with the first multifrequnecy transcranial doppler," Stroke, vol. 33, pp. 1969-1974, 2002.
7. G. Devuyst, P. Despland, and J. Bougousslavsky, "The matching pursuit: A new method of characterizing microembolic signal," Ultrasound Med. Biol., vol. 26, pp. 1051-1056, 2002.
8. J. Smith, D. Evans, L. Fan, A. Thrush, and A. Naylor, "Processing doppler ultrasound signals from blood-borne emboli," Ultrasound Med. Biol., vol. 20, pp. 455-462, 1994.
9. E. Roy, P. Abraham, S. Montresor, and J. Saumet, "Comparaison of time-frequency estimators for peripheral embolus detection," Ultrasound Med. Biol., vol. 26, pp. 419-423, 2000.
10. C. Guetbi, D. Kouame, A. Ouahabi, and J. Remenieras, "New emboli detection methods," in Proc. IEEE Int. Ultrason. Symp., 1997, vol. 2, pp. 1119-1122.
11. P. Lui, B. Chan, F. Chan, P. Poon, H. Wang, and F. Lam, "Wavelet analysis of embolic heart sound detected by precordial doppler ultrasound during continuous venous air embolism in dogs," Anesth. Analg., vol. 2, pp. 325-331, 1998.
12. N. Aydin, S. Padayachee, and H. Markus, "The use of the wavelet transform to describe embolic signals," Ultrasound Med. Biol., vol. 25, pp. 953-958, 1999.
13. B. Krongold, A. Sayeed, M. Moehring, J. Ritcey, M. Spencer, and D. L. Jones, "Time-scale detection of microemboli in flowing blood with doppler ultrasound," IEEE Trans. Biomed. Eng., vol. 46, no. 9, pp. 1081-1089, Sep. 1999.
14. J. Girault, D. Kouamé, A. Ouahabi, and F. Patat, "Micro-emboli detection: An ultrasound doppler signal processing view point," IEEE Trans. Biomed. Eng., vol. 47, no. 11, pp. 1431-1438, Nov. 2000.
15. L. Fan, D. Evans, and A. Naylor, "Automated embolus identification using a rule-based expert system," Ultrasound Med. Biol, vol. 27, no. 8, pp. 1065-1077, 2001.
16. V. Kemeny, D. W. Droste, S. Hermes, D. G. Nabavi, G. S.-Altedorneburg, M. Siebler, and E. B. Ringelstein, "Automatic embolus detection compared with human experts : A doppler ultrasound study," Stroke, vol. 27, pp. 1840-1843, 1996.
17. J. J. A., Estimation of Blood Velocities Using Ultrasound. A Signal Processing Approach. Cambridge, U.K.: Cambridge Univ. Press, 1996.
18. D. Kouamé, J.-M. Girault, A. Ouahabi, and F. Patat, "Reliability evaluation of emboli detection using a statistical approach," in Proc. IEEE Utrason. Symp., vol. 2, 1999, pp. 1601-1604.
19. L. Zadeh, "Fuzzy set," Inform. Control, vol. 8, pp. 338-353, 1965.
20. C. Lin and C. Lee, "Neural-network-based fuzzy logic control and decision system," IEEE Trans. Comput., vol. 40, no. 12, pp. 1320-1336, Dec. 1991.
21. J. Jang, "Anfis : Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-683, May-Jun. 1993.
22. M. O. Efe and O. Kaynak, "A novel optimization procedure for training of fuzzy inference systems by combining variable structures systems technique and levenberg marquardt algorithm," Fuzzy Sets Syst., vol. 122, pp. 153-165, 2001.
23. M. Hagan and M. Menhaj, "Training feedforward networks with markardt algorithm," IEEE Trans. Neural Netw., vol. 5, no. 6, pp. 989-993, Nov. 1994.
24. G. Lera and M. Pinzolas, "Neighborhood based levenberg marquardt algorithm for neural network training," IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1200-1203, Sep. 2002.
25. A. Palit and R. Babuska, "Efficient training algorithm for takagi-sugeno type neuro-fuzzy network," in Proc. 10th IEEE Int. Conf. Fuzzy Syst., vol. 3, 2001, pp. 1367-1371.
26. D. Kouamé, J. Roux, and A. Ouahabi, "Parametric estimation: Improvement of the rls approach using a differential approach," Int. J. Model Simulation, vol. 19, pp. 18-26, 1999.
27. D. Kouamé, J. Girault, V. Labat, and A. Ouahabi, "Delta high order cumulant-based recursive instrumental variable algorithm," IEEE Signal Process. Lett., vol. 7, no. 9, pp. 262-265, Sep. 2000.
28. S. Niu, D. Fisher, and D. Xiao, "A factored form of instrumental variable algorithm," in Int. J. Adapt. Control Signal Process., vol. 7, pp. 261-273.