title: Classification of transcranial Doppler signals using artificial neural network.
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.
authors.affiliation: Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey.
authors.forename: Selami
authors.initials: S
authors.lastname: Serhatlioğlu
authors.forename: Firat
authors.initials: F
authors.lastname: Hardalaç
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
mesh_terms: D000328:Adult; D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology; D001921:Brain / Q000502:physiology*; D002648:Child; D004453:Echoencephalography; D005583:Fourier Analysis; D006801:Humans; D016571:Neural Networks, Computer; D012683:Sensory Deprivation; D018608:Ultrasonography, Doppler / Q000379:methods*; D014481:United States