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Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals by Elif Derya Übeyli; İnan Güler is a Medicine article available to read on EtoBox.

What is Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals about?

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 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. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the resu

Who reads Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals?

It is typically read by researchers, students, and practitioners in Medicine.

Author
Elif Derya Übeyli; İnan Güler
Publisher
Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0010-4825)
Published
2005
Language
EN
Field
Medicine (Physical Sciences)

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