Artificial Intelligence in Medicine
Volume 40, Issue 2 , Pages 143-156 , June 2007

Complex-valued wavelet artificial neural network for Doppler signals classifying

  • Yüksel Özbay

      Affiliations

    • Selcuk University, Department of Electronics Engineering, 42075 Konya, Turkey
    • Corresponding Author InformationCorresponding author. Tel.: +90 332 223 20 48; fax: +90 332 241 06 35.
  • ,
  • Sadık Kara

      Affiliations

    • Erciyes University, Department of Electronics Engineering, 38039 Kayseri, Turkey
  • ,
  • Fatma Latifoğlu

      Affiliations

    • Turkish Standards Institution, Organized Industry Area, 6 Street, 38512 Kayseri, Turkey
  • ,
  • Rahime Ceylan

      Affiliations

    • Selcuk University, Department of Electronics Engineering, 42075 Konya, Turkey
  • ,
  • Murat Ceylan

      Affiliations

    • Selcuk University, Department of Electronics Engineering, 42075 Konya, Turkey

Received 11 July 2006 ,Revised 19 January 2007 ,Accepted 8 February 2007.

References 

  1. Hirai T, Sasayama S, Kawasaki T, Yagi S. Stiffness of systemic arteries in patients with myocardial infarction. A noninvasive method to predict severity of coronary atherosclerosis. Circulation. 1989;80:78–86
  2. Stefanadis C, Stratos C, Boudoulas H, Kourouklis C, Toutouzas P. Distensibility of the ascending aorta, comparison of invasive and non-invasive techniques in healthy men and in men with coronary artery disease. Eur Heart J. 1990;11:990–996
  3. Dart AM, Lacombe F, Yeoh JK, Cameron JD, Jennings GL, Laufer E, et al. Aortic distensibility in patients with isolated hyper-cholesterolaemia, coronary artery disease, or cardiac transplant. Lancet. 1991;338:270–273
  4. Avolio AP, Chen S, Wang R. Effects of aging on changing arterial compliance and left ventricular load in a northern Chinese urban comm. Circulation. 1983;50–58
  5. Heints B, Gillessen T, Walkenhorst F. Evaluation of segmental elastic properties of the aorta in normotensive and medically treated hypertensive patients by intravascular ultrasound. J Hypertens. 1993;11:1253–1258
  6. Liu Z, Ting C, Zhu S, Yin FCP. Aortic compliance in human hyper-tension. Hypertension. 1989;14:129–136
  7. Airaksinen KEJ, Salmela PI, Linnaluoto MK. Diminished arterial elasticity in diabetes: Association with fluorescent advanced glycosylation end products in collagen. Cardiovasc Res. 1993;27:942–945
  8. Lehmann ED, Watts GF, Gosling RG. Aortic distensibility and hyper-cholesterolaemia. Lancet. 1992;340:1171–1172
  9. Stefanadis C, Tsiamis E, Vlachopoulos C. Unfavorable effect of smoking on the elastic properties of the human aorta. Circulation. 1997;95:31–38
  10. Syeda B, Gottsauner-Wolf M, Denk S, Pichler P, Khorsand A, Glogar D. Arterial compliance: a diagnostic marker for atherosclerotic plaque burden. Am J Hypertens. 2003;16:356–362
  11. Hoskins PR, McDicken WN, Allan PL. Haemodynamics and blood flow. Clin Doppler Ultrasound. 2000;27–38
  12. Schoen FJ, Cotran RS. Blood vessels pathologic basis of disease. Philadelphia: W.B. Saunders Company Press; 1999;
  13. Libley P. Prevention and treatment of atherosclerosis. In:  Braunwald E,  Fauci AS,  Kasper DL,  Hauser SL,  Longo DL,  Jameson JL editor. Harrison's principles of internal medicine. London: McGraw-Hill; 2001;
  14. Evans D. Doppler signal analysis. Ultrasound Med Biol. 2000;26:13–15
  15. Miller AS, Blott BH, Hames TK. Review of neural network applications in medical imaging and signal processing. Med Biol Eng Comput. 1992;30:449–464
  16. Baxt WG. Use of an artificial neural network for data analysis in clinical decision making, the diagnosis of acute coronary occlusion. Neural Comput. 1990;2:480–489
  17. Allen J, Murray A. Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms. Physiol Meas. 1993;14:13–22
  18. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet. 1995;346:1135–1138
  19. Edenbrandt L, Heden B, Pahlm O. Neural networks for analysis of ECG complexes. J Electrocardiol. 1993;26:66–73
  20. Dybowski R, Gant V. Artificial neural networks in pathology and medical laboratories. Lancet. 1995;346:1203–1207
  21. Baykal N, Reggia JA, Yalabık N, Erkmen A, Beksac MS. Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms. Comput Med Biol. 1996;26:451–462
  22. Siebler M, Rose G, Sitzer M, Bender M, Steinmetz H. Real-time identification of cerebral microemboli with us feature detection by a neural network. Radiology. 1994;3:739–742
  23. Serhatlioğlu S, Hardalaç F, Guler I. Classification of transcranial Doppler signals using artificial neural network. J Med Syst. 2003;27:205–214
  24. Turkoğlu I, Arslan A, Ilkay E. An intelligent system for diagnosis of the heart valve diseases. Expert Syst Appl. 2002;23:229–236
  25. Turkoğlu I, Arslan A, Ilkay E. An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks. Comput Biol Med. 2003;33:319–331
  26. Ubeyli ED, Guler I. Neural network analysis of internal carotid arterial Doppler signals: predictions of stenosis and occlusion. Expert Syst Appl. 2003;25:1–13
  27. Li C, Liao X, Yu J. Complex-valued wavelet network. J Comput Syst Sci. 2003;67:623–632
  28. Benvenuto N, Marchesi M, Piazza F, Uncini A. A comparison between real and complex-valued neural networks in communication applications. In:  Simula O editors. Proceedings of the international conference on neural networks. Amsterdam (Espoo, Finland): Elsevier; 1991;
  29. Georgin N, Koutsougeras C. Complex domain backpropagation. IEEE Trans Circ Syst. 1992;39:330–334
  30. Haykin S. Adaptive filter theory. New Jersey, USA: Prentice Hall; 2002;
  31. Ceylan M, Ceylan R, Dirgenali F, Kara S, Ozbay Y. Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Comput Biol Med. 2007;37:28–36
  32. Kara S. A study of mitral and tricuspid valve blood flows by autoregressive spectral analysis method and Doppler unit. Thesis of Doctorate, Institute of Science of Erciyes University Press; 1995.
  33. Dirgenali F, Kara S, Erdoğan N, Okandan M. Comparison of the autoregressive modeling and fast Fourier transformation in demonstrating Doppler spectral waveform changes in the early phase of atherosclerosis. Comput Biol Med. 2005;35:57–66
  34. Evans DH, Skidmore WN. Doppler ultrasound: physics instrumentation and clinical applications. United Kingdom: John Wiley & Sons; 1989;
  35. Proakis JG, Rader CM, Fuyun L, Chrysostomos L. Advanced digital signal processing. New York, USA: Macmillan; 1992;
  36. Nitta T. An extension of the back-propagation algorithm to complex numbers. Neural Network. 1997;10:1391–1415
  37. Nitta T. A back-propagation algorithm for complex numbered neural networks. In:  Okabe Y editors. Proceedings of 1993 international joint conference on neural networks. New Jersey, USA (Nagoya, Japan): IEEE; 1993;p. 1649–1652
  38. Nitta T. An analysis of the fundamental structure of complex-valued neurons. Neural Process Lett. 2000;12:239–246
  39. Allen DM. The relationship between variable selection and data augmentation and a method for prediction. Technometrics. 1974;16:125–127
  40. Özbay Y, Ceylan R, Karlik B. A fuzzy clustering neural network architecture for classification of ECG arrhytmias. Comput Biol Med. 2006;36:376–388
  41. Özbay Y, Ceylan M. Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Comput Biol Med. 2007;37:287–295
  42. Friedman CP, Wyatt JC. Evaluation methods in medical informatics. New York: Springer Verlag; 1997;
  43. Tarassenko L, Khan YU, Holt MRG. Identification of interictal spikes in the EEG using neural network analysis. In:  Gavan S editors. IEEE proceedings, science, measurement and technology. United Kingdom: IET; 1998;p. 270–278
  44. Dirgenali F, Kara S. Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals. Expert Syst Appl. 2006;31:643–651
  45. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861–874
  46. Minsky ML, Papert SA. Perceptrons. Cambridge: MIT Press; 1969;
  47. Nitta T. Solving the XOR problem and the detection of symmetry using a single complex-valued neuron. Neural Networks. 2003;16:1101–1105
  48. Chen X, Tang Z, Variappan C, Li S, Okada T. A modified error backpropagation algorithm for complex-value neural networks. Int J Neural Syst. 2005;15:435–443
  49. Paul S, Addison . Wavelet transform and the ECG: a review. Physiol Meas. 2005;26:155–199

PII: S0933-3657(07)00014-0

doi: 10.1016/j.artmed.2007.02.001

Artificial Intelligence in Medicine
Volume 40, Issue 2 , Pages 143-156 , June 2007