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The article discusses a study which described the development of a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) to classify the internal carotid artery Doppler signals obtained from several subjects. Topics covered include preference for using Doppler ultrasound for investigation of the artery conditions, the feature extraction and classification parts of the system and the effectiveness of the LVQ NN method.

This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The CNN models achieved accuracy rates which were higher than that of the stand-alone neural network models. [ABSTRACT FROM AUTHOR]

In this study, Doppler signals recorded from the output of carotid artery of 30 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform (FFT) and least squares autoregressive (AR) methods to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation. [ABSTRACT FROM AUTHOR]

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. Therefore, Doppler ultrasonography is known as reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, arterial Doppler signals recorded from 105 subjects were processed by PC-computer using fast Fourier transform, Burg autoregressive, and least squares modified Yule–Walker autoregressive moving average methods. Doppler power spectrums of arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler power spectrums as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in arteries. Reliable information on hemodynamic alterations in arteries can be obtained by evaluation of these sonograms. [ABSTRACT FROM AUTHOR]