Wavelet-based neural network analysis of ophthalmic artery Doppler signals

Guler N., Ubeyli E.

COMPUTERS IN BIOLOGY AND MEDICINE, vol.34, no.7, pp.601-613, 2004 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 7
  • Publication Date: 2004
  • Doi Number: 10.1016/j.compbiomed.2003.09.001
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.601-613
  • Keywords: Doppler signals, wavelet transform, multilayer perceptron neural network, Levenberg-Marquardt algorithm, ophthalmic artery, ULTRASOUND, CLASSIFICATION, DIAGNOSIS, STENOSIS
  • Gazi University Affiliated: No


In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis. (C) 2003 Elsevier Ltd. All rights reserved.