NDLI: Comparison of neuro-fuzzy approaches with artificial neural networks for the detection of Ischemia in ECG signals

Content Provider	IEEE Xplore Digital Library
Author	Tonekabonipour, H. Emam, A. Teshnelab, M. Aliyari Shoorehdeli, M.
Copyright Year	2010
Description	Author affiliation: Mechatronics Department, Qazvin Islamic Azad University, Iran (Tonekabonipour, H.; Emam, A.) \|\| Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran (Teshnelab, M.; Aliyari Shoorehdeli, M.)
Abstract	This paper compares different classification methods of ECG signals including their accuracies. First of all , Preprocessing for ECG signal is necessary in order to detect QRS complex. Then, with the intention of extract influential features in Ischemia disease, baseline wandering and noise suppression is done. Following to above mentioned target, two neuro-fuzzy classification algorithms incorporated with two artificial neural networks classifiers selected. They put under test to investigate their ability to recognize Ischemic Heart Disease (IHD) from ECG signals. Adaptive Neuro Fuzzy Inference System (ANFIS) and Locally Linear Model Tree (LOLIMOT), are two neuro-fuzzy networks used in the test. They have good capability of learning. Also Multi layer Perceptron (MLP) and Probabilistic Neural Networks (PNN) used as well in test. These are four structures totally used in this paper. The ECG sampled signals are taken from MIT-BIH database. They are used to train neural networks enabling them to classify Ischemia. All neuro-structures have been tested by using experimental ECG records of individuals. The results suggest that neuro-fuzzy classifiers perform better than the other types of classifiers.
Starting Page	4045
Ending Page	4048
File Size	646222
Page Count	4
File Format	PDF
ISBN	9781424465866
ISSN	1062922X
e-ISBN	9781424465880
DOI	10.1109/ICSMC.2010.5642196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-10
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Neural Network ECG Classification Neuro-Fuzzy
Content Type	Text
Resource Type	Article

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