Abstract
In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Evans, D. H., McDicken, W. N., Skidmore, R., and Woodcock, J. P., Doppler Ultrasound: Physics, Instrumentation and Clinical Applications, Wiley, Chichester, 1989.
Sigel, B., A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound Med. Biol. 24(2):169–176, 1998.
Müller, M., Ciccotti, P., Reiche, W., and Hagen, T., Comparison of color-flow Doppler scanning, power Doppler scanning, and frequency shift for assessment of carotid artery stenosis. J. Vasc. Surg., 34(6):1090–1095, 2001.
Vaitkus, P. J., Cobbold, R. S. C., and Johnston, K. W., A comparative study and assessment of Doppler ultrasound spectral estimation techniques part II: methods and results. Ultrasound Med. Biol. 14(8):673–688, 1988.
Wright, I. A., Gough, N. A. J., Rakebrandt, F., Wahab, M., and Woodcock, J. P., Neural network analysis of Doppler ultrasound blood flow signals: A pilot study. Ultrasound Med. Biol. 23(5):683–690, 1997.
Wright, I. A., and Gough, N. A. J., Artificial neural network analysis of common femoral artery Doppler shift signals: Classification of proximal disease. Ultrasound Med. Biol. 24(5):735–743, 1999.
Akay, M., Akay, Y. M., and Welkowitz, W., Automated noninvasive detection of coronary artery disease using wavelet-based neural networks, IEEE Proc. 16th Ann. Int. Conf. Eng. Advances. New Opportunities for Biomedical Engineers, Engineering in Medicine and Biology Society, 1:A12–A13, 3–6 November, 1994.
Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan, New York, 1994.
Basheer, I. A., and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 43:3–31, 2000.
Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30:449–464, 1992.
Baxt, W. G., Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Comput. 2:480–489, 1990.
Chaudhuri, B. B., and Bhattacharya, U., Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing, 34:11–27, 2000.
Hagan, M. T., and Menhaj, M. B., Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5(6):989–993, 1994.
Battiti, R., First- and second-order methods for learning: Between steepest descent and Newton’s method. Neural Comput. 4: 141–166, 1992.
Baykal, N., Reggia, J. A., Yalabik, N., Erkmen, A., and Beksac, M. S., Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms. Comput. Biol. Med. 26(6), 451–462, 1996.
Beksac, M. S., Başaran, F., Eskiizmirliler, S., Erkmen, A. M., and Yörükan, S., A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms. Eur. J. Obstet. Gynecol. Reprod. Biol. 64, 37–42, 1996.
Smith, J. H., Graham, J., and Taylor, R. J., The application of an artificial neural network to Doppler ultrasound waveforms for the classification of arterial disease. Int. J. Clin. Monitoring Comput. 13, 85–91, 1996.
Güler, İ., and Übeyli, E. D., Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Comput. Biol. Med. 33(4):333–343, 2003.
Übeyli, E. D., and Güler, İ., Neural network analysis of internal carotid arterial Doppler signals: Predictions of stenosis and occlusion. Expert. Sys. Appl. 25(1):1–13, 2003.
Daubechies, I., The wavelet transform, time-frequency localization and signal analysis. IEEE Transact. Inf. Theory 36(5):961–1005, 1990.
Akay, M., Wavelet applications in medicine. IEEE Spectrum, 34(5):50–56, 1997.
Akay, M., and Mello, C., Wavelets for biomedical signal processing, IEEE Proc. 19th Ann. Int. Conf. Eng. in Med. and Biol. Soc. 6:2688–2691, Chicago, 30 October–2 November, 1997.
Zhang, Y., Wang, Y., Wang, W., and Liu, B., Doppler ultrasound signal denoising based on wavelet frames, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 48(3):709–716, 2001.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Übeyli, E.D., Güler, İ. Wavelet-Based Neural Network Analysis of Internal Carotid Arterial Doppler Signals. J Med Syst 30, 221–229 (2006). https://doi.org/10.1007/s10916-005-7992-1
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10916-005-7992-1