• Anamica Tejpal
• Kamaljit kaur

Electrocardiogram (ECG) is alluded as change in heart rate which can be high or low sometimes as compared to normal heart rate. This change leads to various heart disease. So this has significance in human beings. different researchers introduced different classifiers to classify the ECG datasets. In this paper we have proposed an algorithm deep learning artificial neural network. ECG datasets contains the pattern data because of it, neural networks work better but based on deep learning artificial neural network algorithm has been enhanced with the use of back propagation. This enhanced algorithm has been compared with other classifiers and with the use of best algorithm other parameters such as accuracy, true positive, false negative, false positive, specificity, ROC curve and many more parameters has been computed. The output results in better efficiency of deep learning artificial neural network(DLANN)

Electrocardiogram,Classification, Arrhythmia,HRV, Heart Rate,Deep Learning, Neural Network.

"ECG ARRHYTHMIA CLASSIFICATION USING ARTIFICIAL DEEP LEARNING NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.164-170, May-2019, Available :http://www.jetir.org/papers/JETIR1905K22.pdf

"ECG ARRHYTHMIA CLASSIFICATION USING ARTIFICIAL DEEP LEARNING NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp164-170, May-2019, Available at : http://www.jetir.org/papers/JETIR1905K22.pdf