Electrocardiogram Features Extraction and Classification for Arrhythmia Detection
نویسندگان
چکیده
This paper present a new automated detection method for cardiac arrhythmia. The detection system is implemented with integration of feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using autoregressive (AR) and multivariate autoregressive (MVAR) modeling of one-lead and two-lead electrocardiogram signals. Obtained features are used as input to the classifier. The classification is performed using a quadratic discriminant function (QDF) and a multilayer perceptron (MLP). The results show that the MVAR coefficients produce the best accuracy rate. Keywords—arrhythmia classification; feature extraction; MIT-BIH database; multilayer perceptron; multivariate autoregressive modeling.
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