Sanjay Nalbalwar

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Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately causes irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural(More)
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method uses Modular neural network (MNN) model to classify arrhythmia into normal and abnormal classes. We have performed experiments on UCI Arrhythmia data set [8]. Missing attribute values of this data set are replaced by closest column value of the(More)
Evaluation is the key to making real progress in machine learning. In this paper we have evaluated performance of our proposed approach for cardiac arrhythmia disease classification from standard 12 lead ECG recordings data, using a Generalized Feedforward Neural Network (GFNN) model. The proposed classifier is trained using static backpropagation algorithm(More)
Today heart disease is the most common cause of death in the world so the detection and treatment of arrhythmias has become one of the biggest challenges for cardiac care unit. Artificial neural networks have been successfully applied to classify medical or diagnostic data. The main objective in this paper is to distinguish between the normal and abnormal(More)
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