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- W2891272861 abstract "Electrocardiogram (ECG) arrhythmia is referred to as a change in human heart rhythm, and it becomes either too slow or very large compared to normal heart rhythms. This may cause disease affecting cardiac. Early correct identification of arrhythmia is important in the detection of cardiac disease and getting the better treatment of a patient. Numerous classifiers are present for ECG diagnosis. Artificial neural network (ANN) is one of the more popular and very widely utilized models for ECG diagnosis. In this paper, we introduced three different ANN models, which are classified as healthy and arrhythmia classes and using UCI repository ECG 12 lead signal feature extracted data. This particularly uses ANN models that are trained as well as tested on back-propagation feedforward neural network, recurrent neural network (RNN), and radial basis function (RBF) networks. We evaluated the diagnosis testing result in the form of classification accuracy, sensitivity, and specificity. Among these three contrast ANN models, RNN models have shown better diagnosis result up to obtained 83.1% testing classification accuracy with selected attributes." @default.
- W2891272861 created "2018-09-27" @default.
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- W2891272861 date "2018-09-08" @default.
- W2891272861 modified "2023-09-23" @default.
- W2891272861 title "ECG Arrhythmia Classification Using Artificial Neural Networks" @default.
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- W2891272861 doi "https://doi.org/10.1007/978-981-13-1217-5_63" @default.
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