Rudolph, R. E., and Kowdley, K. V., Cirrhosis of the Liver. In: Conn, R. B. ET AL (Ed.), _Current Diagnosis 9_Saunders, Philadelphia, 1997. Google Scholar
Worman, H. J., The Liver Disorders Sourcebook. NTC/Contemporary, DeSoto, 1999. Google Scholar
Matthew, C., Handbook of Diagnostic Tests. Springhouse, Spring House, 1995. Google Scholar
Bates, B., A Guide to Physical Examination and History Taking. Lippincott, Philadelphia, 1995. Google Scholar
Friedman, S. L., Liver biliary tract and pancreas. In: Tierney, L. M. Jr. ET AL (Ed.), Current Medical Diagnosis & Treatment. Appleton & Lange, Stamford, 1997. Google Scholar
Berkow, R., Hepatic and biliary disorders: Neoplasms of the liver. In: The Merck Manual of Diagnosis and Therapy. Merck Research Laboratories, Rahway, 1992. Google Scholar
Alpern, M. B., Rubin, J. M., Williams, D. M., and Capek, P., Porta hepatis: Duplex Doppler US with angiographic correlation. Radiology. 162:53–56, 1987. Google Scholar
Iwao, T., Oho, K., and Sakai, T., Splanchnic and extrasplanchnic arterial hemodynamics in patients with cirrhosis. J. Hepatol. 27:817–823, 1997. Article Google Scholar
Moriyasu, F., Nishida, O., and Ban, N., Measurement of portal vascular resistance in patients with portal hypertension. Gastroenterology. 90:710–717, 1986. Google Scholar
Piscaglia, F., Gaiani, S., and Gramantieri, L., Superior mesenteric artery impedance in chronic liver diseases: Relationship with disease severity and portal circulation. Am. J. Gastroenterol. 93:1798–1799, 1998. Article Google Scholar
Edenbrandt, L., Heden, B., and Pahlm, O., Neural networks for analysis of ECG complexes. J. Electrocardiol. 26:66–73, 1993. Article Google Scholar
Siebler, M., Rose, G., Sitzer, M., Bender, A., and Steinmetz, R., Realtime identification of cerebral microemboli with ultrasound feature detection by a neural network. Radiology. 192:739–742, 1994. Google Scholar
Abel, E. W., Zacharia, P. C., Forster, A., and Farrow, T. L., Neural network analysis of the EMG interference pattern. Med. Eng. Phys. 18:12–17, 1996. Article Google Scholar
Wright, I. A., and Gough, N. A. J., Artificial neural network analysis of common femoral artery Doppler shift signals: Classification of proximal disease. Ultarsound. Med. Biol. 24:735–743, 1999. Article Google Scholar
Mobley, B. A., Schechter, E., Moore, W. E., McKee, P. A., and Einchner, J. E., Predictions of coronary artery stenosis by artificial neural network. Artif. Intell. Med. 18:187–203, 2000. Article Google Scholar
Kara, S., Kemaloglu, S., and Guven, A., Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network. Expert. Syst. Appl. 29:945–952, 2005. Article Google Scholar
İçer, S., and Kara, S., Detection of cirrhosis by short time Fourier transform using portal vein Doppler signals, INISTA 2005 International Symposium on Innovations and Applications, pp. 17–20, İstanbul, Turkey.
İçer, S., Kara, S., and Güven, A., Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease. Expert. Syst. Appl. 31:406–413, 2006. Article Google Scholar
İçer, S., and Kara, S., Spectral analysing of portal vein Doppler signals in the cirrhosis patients. Comput. Biol. Med. 37:1303–1307, 2007. Article Google Scholar
Kara, S., İçer, S., Akdemir, B., and Polat, K., Intelligent Detection System to Diagnose of Cirrhosis Disease: Combining Generalized Discriminant Analysis and Artificial Immune Recognition System, DCDIS Proceedings of the International Conference on Life System Modeling and Simulation (LSMS2007), pp 28–32.
Georgiou, G. M., and Koutsougeras, C., Complex domain backpropagation. IEEE Trans. Circuits Syst.-II: Analog Digit. Signal Process. 39:330–334, 1992. ArticleMATH Google Scholar
Nitta, T., A complex numbered version of the back-propagation algorithm. Proceedings of INNS World Congress on Neural Networks. 3:576–579, 1993. Google Scholar
Chen, X., Tang, Z., Variappan, C., Li, S., and Okada, T., A modified error backpropagation algorithm for complex-value neural networks. Int. J. Neural. Syst. 15:435–443, 2005. Article Google Scholar
Benvenuto, N., and Piazza, F., On the complex backpropagation algorithm. IEEE Trans. Signal Process. 40:967–969, 1992. Article Google Scholar
Hirose, A., Proposal of fully complex-valued neural networks. Proc. Int. Jt. Conf. Neural Netw. 3:27–31, 1992. Google Scholar
Kim, M. S., and Guest, C. C., Modification of backpropagation networks for complex-valued signal processing in frequency domain. Proc Int Jt. Conf. Neural Networks. 3:27–31, 1990. Article Google Scholar
Nitta, T., An extension of the back-propagation algorithm to complex numbers. Neural Netw. 10:1392–1415, 1997. Article Google Scholar
Benvenuto, N., Marchesi, M., Piazza, F., Uncini, A., A comparison between real and complex-valued neural networks in communication applications. Proc Int Conf Neural Netw. Espoo, 1991.
Hirose, A., and Hiramatsu, K., Proposal of complex-valued region-based-coupling segmentation neural networks and the application to radar imaging systems. IJCNN Neural Netw. 1:146–151, 2000. Google Scholar
Ceylan, M., Cetinkaya, N., Ceylan, R., and Özbay, Y., Comparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysis. Lecture Notes in Artificial Intelligence. 3949:92–99, 2006. Article Google Scholar
Azevedo, F. D., Travessa, S. S., Argoud, F.I.M., The investigation of complex neural network on epileptiform pattern classification. Proc. The 3rd European Medical and Biological Engineering Conference, pp 2800–2804, 2005.
Ceylan, M., Ceylan, R., Dirgenali, F., Kara, S., and Özbay, Y., Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Comput. Biol. Med. 37:28–36, 2007. Article Google Scholar
Özbay, Y., and 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. 37:287–295, 2007. Article Google Scholar
Nitta, T., An analysis of the fundamental structure of complex-valued neurons. Neural Process. Lett. 12:239–246, 2000. ArticleMATH Google Scholar
Güler, I., Hardalaç, F., and Kaymaz, M., Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from cerebral vessels. Comput. Biol. Med. 32:445–453, 2002. Article Google Scholar
Haris, F. J., On the use of Windows for harmonic analysis with discrete Fourier transform. Proc. IEEE. 66:51–83, 1978. Article Google Scholar
Kay, S. M., Modern Spectral Estimation. Prentice Hall, Englewood Cliffs, 1988. MATH Google Scholar
Aydın, N., and Markus, H. S., Optimization of processing parameters for the analysis and detection of embolic signals. Eur. J. Ultrasound. 12:69–79, 2000. Article Google Scholar
Allen, D. M., The relationship between variable selection and data augmentation and a method for prediction. Technometrics. 16:125–127, 1974. ArticleMATHMathSciNet Google Scholar
Özbay, Y., Ceylan, R., and Karlik, B., A fuzzy clustering neural network architecture for classification of ECG arrhytmias. Comput. Biol. Med. 36:376–388, 2006. Article Google Scholar
Tarassenko, L., Khan, Y. U., and Holt, M. R. G., Identification of interictal spikes in the EEG using neural network analysis. Proceedings, Science, Measurement and Technology. IEEE. 45:270–278, 1998. Google Scholar
Dirgenali, F., and Kara, S., Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals. Expert. Syst. Appl. 31:643–651, 2006. Article Google Scholar