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Abstract
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Article Information:
Using Neural and Fuzzy Software for the Classification of ECG Signals
Saad Alshaban and Rawaa Ali
Corresponding Author: Saad Alshaban
Submitted: 2009 June, 11
Accepted: 2009 October, 28
Published: 2010 January, 05 |
Abstract:
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Two approaches to classify the ECG biomedical signals are presented in this work. One is the
Artificial Neural Network (ANN) with multilayer perceptron and the other is the Fuzzy Logic with Fuzzy
Knowledge Base Controller (FKBC). Backpropagation Learning Algorithm (BPA) has been used at preset to
train the ANN. MATLAB version 6.5 program was used. The ECG signals were classified to eleven groups,
one of them is for the normal cases and the others represent ten different diseases. These ECG records were
taken for the patients of the Surgical Specialization Center. These ECG records were divided into two groups
one for training the systems and the other is for testing them. The performance of both systems, i.e. the ANN
and the FL, was evaluated for different examples and Both programs give classification for all the cases. W ith
average percentage of error between the training data group and the testing one is 4.793%. FL system takes
fewer time to classify the ECG signals than the ANN because the Knowledge in the NN is automatically
acquired by the BPA, but the learning process is relatively slow and the analysis of the trained network was
found difficult.
Key words: Back propagation algorithm (BPA), ECG Signal, fuzzy software, neural network, , ,
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Abstract
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Cite this Reference:
Saad Alshaban and Rawaa Ali, . Using Neural and Fuzzy Software for the Classification of ECG Signals. Research Journal of Applied Sciences, Engineering and Technology, (1): Page No: 5-10.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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