Author(s): Abhinav Vishwa | Mohit K. Lal | Sharad Dixit | Pritish Vardwa
Journal: International Journal of Interactive Multimedia and Artificial Intelligence
ISSN 1989-1660
Volume: 1;
Issue: 4;
Start page: 67;
Date: 2011;
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Keywords: accuracy | artificial neural networks | ECG arrhythmia | rule.Arrhythmia classification | sensitivity | specificity
ABSTRACT
In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. Neural network model with back propagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for MIT-BIH arrhythmia. The different structures of ANN have been trained by mixture of arrhythmic and non arrhythmic data patient. The classification performance is evaluated using measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC).Our experimental results gives 96.77% accuracy on MIT-BIH database and 96.21% on database prepared by including NSR database also.
Journal: International Journal of Interactive Multimedia and Artificial Intelligence
ISSN 1989-1660
Volume: 1;
Issue: 4;
Start page: 67;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: accuracy | artificial neural networks | ECG arrhythmia | rule.Arrhythmia classification | sensitivity | specificity
ABSTRACT
In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. Neural network model with back propagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for MIT-BIH arrhythmia. The different structures of ANN have been trained by mixture of arrhythmic and non arrhythmic data patient. The classification performance is evaluated using measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC).Our experimental results gives 96.77% accuracy on MIT-BIH database and 96.21% on database prepared by including NSR database also.
