Classification of Transcranial Doppler Signals Using Artificial Neural Network

Authors:
Selami Serhatlioglu;Fırat Hardalaç;İnan Güler
Affiliations:
Department of Radiology, Faculty of Medicine, Fırat University, Elazıg, Turkey;Department of Biophyscs, Faculty of Medicine, Fırat University, Elazıg, Turkey;Department of Electric and Electronic, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2003

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Abstract

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.