کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
535273 870335 2006 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A recurrent neural network classifier for Doppler ultrasound blood flow signals
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A recurrent neural network classifier for Doppler ultrasound blood flow signals
چکیده انگلیسی

The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) trained with Levenberg–Marquardt algorithm on the Doppler ultrasound blood flow signals. The ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The RNNs were implemented for diagnosis of OA and ICA diseases using the statistical features as inputs. We explored the ability of designed and trained Elman RNNs, combined with wavelet preprocessing, to discriminate the Doppler signals recorded from different healthy subjects and subjects suffering from OA and ICA diseases. The classification results demonstrated that the proposed combined wavelet/RNN approach can be useful in analyzing long-term Doppler signals for early recognition of arterial diseases.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 27, Issue 13, 1 October 2006, Pages 1560–1571
نویسندگان
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