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Title: Deep Neural Network Models for Detection of Arrhythmia based on Electrocardiogram Reports
Authors: Ghuge S.
Kumar N.
Shenoy T.
Sowmya Kamath S.
Issue Date: 2020
Citation: 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020 , Vol. , , p. -
Abstract: Electrocardiogram (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.
Appears in Collections:2. Conference Papers

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