Attention-Based Convolutional Denoising Autoencoder for Two-Lead ECG Denoising and Arrhythmia Classification

This article presents a fast and accurate electrocardiogram (ECG) denoising and classification method for low-quality ECG signals. To achieve this, a novel attention-based convolutional denoising autoencoder (ACDAE) model is proposed that utilizes a skip-layer and attention module for reliable recon...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 10
Main Authors:	Singh, Prateek, Sharma, Ambalika
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Arrhythmia Arrhythmias atrial fibrillation (AF) Cardiac stress tests Classification Codes Convolution convolutional neural network (CNN) denoising autoencoder (DAE) electrocardiogram (ECG) Electrocardiography Feature extraction Modules Noise reduction Random noise Recording Signal quality Signal to noise ratio
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
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