Ghuge S.Kumar N.Shenoy T.Sowmya Kamath S.2021-05-052021-05-0520202020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020 , Vol. , , p. -https://doi.org/10.1109/ICCCNT49239.2020.9225534https://idr.nitk.ac.in/handle/123456789/14706Electrocardiogram (ECG) is an indicative technique using which the heartbeat time series of a patient is recorded on the moving strip of paper or line on the screen, for irregularity analysis by experts, which is a time-consuming manual process. In this paper, we proposed a deep neural network for the automatic, real-time analysis of patient ECGs for arrhythmia detection. The experiments were performed on the ECG data available in the standard dataset, MIT-BID Arrhythmia database. The ECG signals were processed by applying denoising, detecting the peaks, and applying segmentation techniques, after which extraction of temporal features was performed and fed into a deep neural network for training. Experimental evaluation on a standard dataset, using the evaluation metrics accuracy, sensitivity, and specificity revealed that the proposed approach outperformed two state-of-the-art models with an improvement of 2-7% in accuracy and 11-16% in sensitivity. © 2020 IEEE.Conference Paper