کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
386953 660893 2009 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents
چکیده انگلیسی

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five 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 saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 36, Issue 5, July 2009, Pages 9031–9038
نویسندگان
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