A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network |
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Authors: | Yüksel Özbay |
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Institution: | (1) Engineering and Architecture Faculty, Department of Electrical & Electronics Engineering, Selcuk University, 42075 Konya, Turkey |
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Abstract: | This paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented
with integration of complex valued feature extraction and classification parts. In feature extraction phase of proposed method,
the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT). The aim of using CWT
is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features
are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types
of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification
tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia
from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second
classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs.
Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged)
and 99.2% for the first and second classification tasks, respectively. All arrhythmias in training and test phases were classified
correctly for both of the classification tasks. |
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