Classification of Transcranial Doppler Signals Using Artificial Neural Network

uler 3,4 Transcranial Doppler signals, recorded from the temporal region of brain on 110 patients were transferred to a personal computer by using a 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently can not offer a good spectral resolution at jet blood flows, it sometimes causes wrong interpretation of transcranial Doppler signals. To do a correct and rapid diagnosis, transcranial Doppler blood flow signals were statistically arranged so that they were classified in artificial neural network. Back propagation neural network and self-organization map algorithms of artificial neural network were used for training, whereas momentum and delta‐bar-delta algorithms were used for learning. The results of these algorithms were compared in the case of classification and learning.

[1]  O Pahlm,et al.  Neural networks for analysis of ECG complexes. , 1993, Journal of electrocardiology.

[3]  P J Benkeser,et al.  A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow , 1996, Comput. Biol. Medicine.

[4]  A. Lupetin,et al.  Transcranial Doppler sonography. Part 1. Principles, technique, and normal appearances. , 1995, Radiographics : a review publication of the Radiological Society of North America, Inc.

[5]  I. Wright,et al.  Artificial neural network analysis of common femoral artery Doppler shift signals: classification of proximal disease. , 1999, Ultrasound in medicine & biology.

[6]  N L Browse,et al.  Transcranial Doppler measurement of middle cerebral artery blood flow velocity: a validation study. , 1986, Stroke.

[7]  W. Nichols,et al.  McDonald's Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles , 1998 .

[8]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[9]  R. Dybowski,et al.  Artificial neural networks in pathology and medical laboratories , 1995, The Lancet.

[10]  G Rose,et al.  Real-time identification of cerebral microemboli with US feature detection by a neural network. , 1994, Radiology.

[11]  Rune Aaslid,et al.  Transcranial Doppler Sonography , 1986, Springer Vienna.

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[13]  R. Adams,et al.  Transcranial Doppler ultrasound , 1990, Neurology.

[14]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  Paul A. Lynn,et al.  An introduction to the analysis and processing of signals , 1973 .

[17]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[18]  T. Treasure,et al.  The Impact of Microemboli During Cardiopulmonary Bypass on Neuropsychological Functioning , 1994, Stroke.