methodology-7

Electrocardiography (ECG)

3260 papers • 126 benchmarks • 313 datasets

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Use these libraries to find electrocardiography-ecg-23 models and implementations

axelmukwena/biometricECG

2 papers 30

Datasets

MIMIC PERform Testing Dataset

MIMIC-IV-ECG

Subtasks

Mortality Prediction ECG Classification Arrhythmia Detection Heart Rate Variability Heart Rate Variability

Most implemented papers

ECG Heartbeat Classification: A Deep Transferable Representation

Mohammad Kachuee, Shayan Fazeli, M. Sarrafzadeh•Wed Apr 18 2018

A method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard is proposed.

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Content

Heartbeat Classification

Myocardial infarction detection

ECG Denoising

QRS Complex Detection

ECG Wave Delineation

Ventricular fibrillation detection

ECG QRS Detection

ECG Patient Identification

Congestive Heart Failure detection

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ECG arrhythmia classification using a 2-D convolutional neural network

T. Jun, Hoang Minh Nguyen, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, Young-Hak Kim•Tue Apr 17 2018

The experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent classification accuracy without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.

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Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Awni Y. Hannun, Pranav Rajpurkar, A. Ng, Masoumeh Haghpanahi, Codie Bourn•Wed Jul 05 2017

An algorithm is developed which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor and builds a dataset with more than 500 times the number of unique patients than previously studied corpora.

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An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

Ikaro Silva, George B. Moody•Tue Sep 23 2014

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet’s software and databases and should easily be able to reproduce, validate, and compare results published based on Physio net's software and database.

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Deep Learning for ECG Classification

B. Pyakillya, N. Kazachenko, N. Mikhailovsky•Sat Sep 30 2017

The proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes.

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Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings

S. Goodfellow, Andrew J. Goodwin, R. Greer, P. Laussen, Mjaye L. Mazwi, D. Eytan•Wed Nov 28 2018

A deep convolutional neural network was trained to classify single lead ECG waveforms as either Normal Sinus Rhythm, Atrial Fibrillation, or Other Rhythm, and class activation mappings were extracted to help better understand which areas of the waveform the model was focusing on when making a classification.

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Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

Yadong Zhang, Xin Chen•Tue Dec 08 2020

The TMF classification model has very good clinical interpretability and the patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.

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Blind ECG Restoration by Operational Cycle-GANs

S. Kiranyaz, M. Gabbouj, A. Khandakar, A. Tahir, M. Chowdhury, Tawsifur Rahman, R. Mazhar, Ozer Can Devecioglu, T. Ince, Junaid Malik, T. Hamid•Fri Jan 28 2022

A novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal.

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Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

Fernando Andreotti, Oliver Carr, Marco A. F. Pimentel, A. Mahdi, M. Vos•Wed Sep 13 2017

The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet. The feature-based classifier obtained an Fi score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.

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