01604nas a2200253   4500000000100000000000100001008004100002260001200043653001900055653001600074653001600090653001300106653003100119100001900150700001400169700001700183700001900200245007500219856007500294300001000369490000600379520095100385022001401336       2011                            d  c12/201110aECG arrhythmia10aSensitivity10aSpecificity10aAccuracy10aArtificial Neural Networks1 aAbhinav Vishwa1 aMohit Lal1 aSharad Dixit1 aPritish Vardwa00aClasification Of Arrhythmic ECG Data Using Machine Learning Techniques  uhttp://www.ijimai.org/journal/sites/default/files/IJIMAI20111_4_11.pdf  a67-700 v13 aIn 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 differen 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.  a1989-1660