• Amit Jain
• Dr.VandanaVikas Thakare
• Dr.Rahul Dubey

For modern medicine, electrocardiogram (ECG) is a key diagnostic evaluation tool of cardiac arrhythmias detections. A deep-learning based classification technique has been proposed in this work to detect the cardiac arrhythmias. This proposed scheme is using deep-learning based network that has been trained previously on a standard ECG data set to do automatic ECG arrhythmia assessments by classifying acceptable ECGs into related cardiac contexts.In simulation result having Confusion Matrix Graph (N, A, O, E it representing four diseases), Confusion Matrix Table to identify accuracy about disease. In Training Performance Graph proved deep learning testing accuracy around 94.26%. In Layered architecture classify different layers used in application of deep neural network convolution.ECG (Electro-cardio-gram) having an important place in medical industries and medical science. But there are many machine based learning to identify diseases using ECG data. The main problem present in Machine learning report is analyzed by doctors it may be possibility prediction or analysis about disease not sure

Deep Leatning, ECG classification, convolution neural networks, cardiac arrhythmia, transfer learning

"A Deep Learning Based Cardiac Arrhythmia Detection System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.f274-f281, December-2021, Available :http://www.jetir.org/papers/JETIR2112535.pdf

"A Deep Learning Based Cardiac Arrhythmia Detection System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppf274-f281, December-2021, Available at : http://www.jetir.org/papers/JETIR2112535.pdf