Wavelet-Based Neural Network Analysis of Internal Carotid Arterial Doppler Signals |
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Authors: | Elif Derya Übeyli İnan Güler |
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Institution: | 1. Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji üniversitesi, 06530, S??üt?zü, Ankara, Turkey 2. Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500, Teknikokullar, Ankara, Turkey
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Abstract: | In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal
carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects,
were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron,
was used for analysis of the internal carotid arterial Doppler signals. Multilayer perceptron neural network (MLPNN) trained
with the Levenberg–Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to
determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet
transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively.
All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid
artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal
carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed
that the MLPNN trained with the Levenberg–Marquardt algorithm was effective to detect internal carotid artery stenosis and
occlusion. |
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Keywords: | Doppler signals Wavelet transform Multilayer perceptron neural network Levenberg– Marquardt algorithm Internal carotid artery |
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