DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

In this paper, an efficient deep convolutional neural network (CNN) architecture is proposed based on depthwise temporal convolution along with a robust end-to-end scheme to automatically detect and classify arrhythmia from denoised electrocardiogram (ECG) signal, which is termed as `DeepArrNet'...

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Bibliographic Details
Main Authors: Tanvir Mahmud, Shaikh Anowarul Fattah, Mohammad Saquib
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Arrhythmia
biosignal analysis
CNN
deep learning
disease detection
ECG
Online Access:https://ieeexplore.ieee.org/document/9104710/

Internet

https://ieeexplore.ieee.org/document/9104710/

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