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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Summary: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 reconstruction of ECG signals from extreme noise conditions. Skip-layer connections are used to reduce information loss while reconstructing the original signal, and a lightweight, efficient channel attention (ECA) module is used to update relevant features retrieved via cross-channel interaction efficiently. The model is trained and tested using four widely available databases. For evaluation, the signals are mixed with simulated additive white Gaussian noise (AWGN) ranging from −20 to 20 dB and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) noise stress test database (NSTDB) noise ranging from −6 to 24 dB. The model outperformed the most cited published works, achieving an average signal-to-noise ratio (SNR) improvement of 19.07 ± 1.67 and a percentage-root-mean-square difference (PRD) of 11.0 % at 0-dB SNR. The model classification performance on 60 000 beats is 98.76% ± 0.44% precision, 98.48% ± 0.58% recall, and 98.88% ± 0.42% accuracy, respectively, using a stratified fivefold cross-validation strategy.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3197757