• D.DEVIPRIYA
• Dr.V.S MEENAKSHI

An automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings has been proposed. In this study, it basically keen on delivering high certain arrhythmia arrangement results to be material in analytic choice decision support systems. Neural network model with back propagation calculation is utilized to group arrhythmia cases into typical and strange classes. Networks models are trained and tested for MIT-BIH arrhythmia. The various designs of ANN have been prepared by combination of arrhythmic and non arrhythmic information 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).

Feature Extraction, ECG arrhythmia, Arrhythmia classification, artificial neural networks.

"AN EFFICIENT DATA MINING ALGORITHM FOR CARDIAC ARRHYTHMIA DETECTION USING ECG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c334-c341, July-2022, Available :http://www.jetir.org/papers/JETIR2207239.pdf

"AN EFFICIENT DATA MINING ALGORITHM FOR CARDIAC ARRHYTHMIA DETECTION USING ECG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc334-c341, July-2022, Available at : http://www.jetir.org/papers/JETIR2207239.pdf