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dc.contributor.authorGhuge S.
dc.contributor.authorKumar N.
dc.contributor.authorShenoy T.
dc.contributor.authorSowmya Kamath S.
dc.identifier.citation2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020 , Vol. , , p. -en_US
dc.description.abstractElectrocardiogram (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.en_US
dc.titleDeep Neural Network Models for Detection of Arrhythmia based on Electrocardiogram Reportsen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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