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A Robust Detection Method of Atrial Fibrillation
2018
2018 Computing in Cardiology Conference (CinC)
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Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. ...
We used Computing in Cardiology Challenge 2017 database as training set and MIT-BIH atrial fibrillation database (AFDB) as test set. ...
clinically accepted 12 lead ECG signals, recorded for a relatively longer duration. ...
doi:10.22489/cinc.2018.268
dblp:conf/cinc/HuZXJYWY18
fatcat:lt3v2lmf6bhijb2gpfiojlvhca
An ECG Signal Classification Method Based on Dilated Causal Convolution
2021
Computational and Mathematical Methods in Medicine
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The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. ...
The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65%. ...
In literature [24] , the authors use the squeeze-and-excitation residual network (SE-ResNet) to detect abnormal occurrence. ...
doi:10.1155/2021/6627939
pmid:33603825
pmcid:PMC7872762
fatcat:sbkyraqj7rcitklaccaxah3eb4
Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation
2022
Bioengineering
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(ResNet) to extract representative features from the input ECG signal. ...
We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. ...
The workflow of the proposed atrial fibrillation (AF) detection method.Figure 4. The workflow of the proposed atrial fibrillation (AF) detection method.
Figure 4 . 4 Figure 4. ...
doi:10.3390/bioengineering9090480
pmid:36135025
pmcid:PMC9495512
fatcat:qb5kro42s5c5llg6crnoakdzrq
Automatic Cardiac Arrhythmia Classification Using Residual Network Combined with Long Short-term Memory
2022
IEEE Transactions on Instrumentation and Measurement
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Previous studies focused on the diagnosis of atrial fibrillation, which is the most common arrhythmia in adults. ...
We performed a crosssubject experiment using AFDB and obtained a statistically higher performance using the proposed method compared with typical machine learning methods. ...
His research interests include remote patient monitoring system using wearable medical devices and bio-signal analysis algorithm. ...
doi:10.1109/tim.2022.3181276
fatcat:uisgruk6dbczxj4zebofc24kgu
A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
2021
Sensors
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of ECG with three labels. ...
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. ...
The ECG signals at the onset of atrial fibrillation, 20 to 30 min before the onset of atrial fibrillation, and normal ECG signals were labelled as AF, PAF, and N diseases, respectively. ...
doi:10.3390/s21155222
fatcat:sbh4u3ntlzdjpm2nzh2fu7mwva
ECG Heartbeat Classification Based on an Improved ResNet-18 Model
2021
Computational and Mathematical Methods in Medicine
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Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training ...
Acknowledgments This publication has emanated from a joint research conducted with the financial support of the S&T Major Project of the Science and Technology Ministry of China under the Grant No. 2017YFE0135700 ...
The ECG signals are fundamentally different from the image data. ...
doi:10.1155/2021/6649970
pmid:34007306
pmcid:PMC8110414
fatcat:hy6fk4tu3rfs3fqqomz742zxe4
Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network
[article]
2022
arXiv
pre-print
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Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis ...
Specifically, we propose a Handcrafted-Rule-enhanced Neural Network (called HRNN) for ECG classification with standard 12-lead ECG input, which consists of a rule inference module and a deep learning module ...
Different from ResNet-34 [4], we use 1D convolution operation since we treat the input ECG as 1-dimensional signals. "Convi, j" represents a convolutional layer with j kernels of size i. ...
arXiv:2206.10592v1
fatcat:55ghwwybwjbati6qf5vuzcag4i
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
[article]
2024
arXiv
pre-print
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Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. ...
The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314. ...
Detect AF patients during sinus rhythm Detection of AF using solely normal ECG signals as data input [12] . [12] employed a ResNet architecture to identify ECG features indicative of AF during sinus ...
arXiv:2403.11405v1
fatcat:7agajjaepfdn5baljn2c4247we
A ResNet based multiscale feature extraction for classifying multi-variate medical time series
2022
KSII Transactions on Internet and Information Systems
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This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. ...
a multi-scale feature extraction method, and apply the SE module to extract features of ECG data. ...
[11] used an end-to-end model combining CNN and a recurrent neural network RNN to classify ECG data into atrial fibrillation and normal sinus heart rate in 2019. ...
doi:10.3837/tiis.2022.05.002
fatcat:kryc6mb22vauloinppcmhbcz4y
Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning
2022
Frontiers in Physiology
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In this work, we propose a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD), which permits training a fine-grained (beat-by-beat) arrhythmia detector with the use of large amounts ...
Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly professional demanding and ...
Ambulatory electrocardiogram (ECG) monitoring with prolonged duration (several days or weeks) provides critical information for early detection and treatment of arrhythmias, especially for transient and ...
doi:10.3389/fphys.2022.850951
pmid:35480046
pmcid:PMC9037749
fatcat:urav5bdoy5dcxmtq46sapessbq
Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification
2021
Journal of Healthcare Engineering
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Finally, the model is used to classify multilabel in large databases. ...
Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. ...
[22] proposed an arrhythmia detection method based on the multiresolution representation of ECG signals by taking four different deep neural networks as four channel models for ECG vector representations ...
doi:10.1155/2021/6630643
pmid:34055274
pmcid:PMC8112932
fatcat:4byvawbfqzeofgalmknm3qc5pm
Generalizable and Robust Deep Learning Algorithm for Atrial Fibrillation Diagnosis Across Ethnicities, Ages and Sexes
[article]
2022
arXiv
pre-print
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The main finding explaining these variations was an impairment in performance in groups with a higher prevalence of atrial flutter (AFL). ...
Performance was higher for female than male and young adults (less than 60 years old) and showed some differences across ethnicities. ...
Our search did not identify any previous studies assessing deep learning (DL) algorithms generalization across ethnicities, ages and sexes for the task of atrial fibrillation (AF) detection. ...
arXiv:2207.09667v1
fatcat:i6iyrc2ybncdbg4acxpk535fhm
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement
[article]
2023
arXiv
pre-print
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Electrocardiography (ECG) signals can be considered as multi-variable time-series. ...
The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases PTB-XL and MIT-BIH. ...
Recent works of [37] , [38] have used multi-scale deep convolutional neural networks with ensemble learning to detect heart arrhythmia from 12-lead ECG. ...
arXiv:2301.10171v2
fatcat:ctxrk3i6cjddvna5pdfqldrfga
A review on Machine, Transfer and Deep learning approaches for ECG classification
2023
Frontiers in Scientific Research and Technology
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Cardiovascular Diseases (CVDs) diagnosis requires an expert interpretation of ECG (Electrocardiogram). The ECG is an essential tool that is used to diagnose CVDs for medical treatment to take place. ...
ECG classification problem comes with some challenges that need to be considered such as noise, feature extraction, segmentation, and classification. ...
The target classes were normal (NOR), noise, atrial fibrillation (AF), atrial premature contraction (APC), ventricular fibrillation (VF), and premature ventricular contraction (PVC). ...
doi:10.21608/fsrt.2022.175348.1075
fatcat:3vi3l374qrcp5nu5s3y7gzmx3y
Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020
2022
Frontiers in Physiology
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Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. ...
performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; ( ...
“Adaptive lead weighted resnet trained with different duration signals for classifying 12-lead ECGs,” in 2020 Computing in Cardiology (Rimini: IEEE), 1–4. ...
doi:10.3389/fphys.2021.811661
pmid:35095568
pmcid:PMC8795785
fatcat:oa54zvu4hjbwxm3q6irbtkftja
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