medical-11

ECG Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in ecg-classification-22

Trend

Dataset

Best Model

Actions

Electrocardiography (ECG) on Telehealth Network of Minas Gerais (TNMG)

PhysioNet Challenge 2021

Libraries

i

Use these libraries to find ecg-classification-22 models and implementations

Datasets

UCR Time Series Classification Archive

PTB Diagnostic ECG Database

ECG Heartbeat Categorization Dataset

PhysioNet Challenge 2021

PhysioNet Challenge 2020

Subtasks

Photoplethysmography (PPG)

Most implemented papers

Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Chao-Han Huck Yang, Pin-Yu Chen, Yun-Yun Tsai•Wed Jun 16 2021

Voice2Series (V2S) is proposed, a novel end-to-end approach that reprograms acoustic models for time series classification, through input transformation learning and output label mapping and it is shown that V2S performs competitive results on 19 time series Classification tasks.

148

Content

PhysioNet Challenge 2020

UCR Time Series Classification Archive

PTB-XL

CODE-15%

MIMIC-IV-ECG

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Paper Graph

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.

153 0

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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.

66 0

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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.

3 0

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A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification

Yunan Wu, Feng Yang, Y. Liu, Xuefan Zha, Shaofeng Yuan•Mon Oct 15 2018

An ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs) to alleviate the overfitting problem in two- dimensional network.

100 0

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Automatic diagnosis of the 12-lead ECG using a deep neural network

Thomas Bo Schön, Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, D. Oliveira, P. R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, P. Macfarlane, W. Meira Jr., A. L. Ribeiro•Mon Apr 01 2019

Results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

876 0

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Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length

Linhai Ma, Liang Liang•Fri Aug 07 2020

A CNN is designed for classification of 12-lead ECG signals with variable length, and three defense methods are applied to improve robustness of this CNN against adversarial noises and white noises, with a minimal reduction in accuracy on clean data.

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ECG beats classification via online sparse dictionary and time pyramid matching

Nanyu Li, Yujuan Si, Duo Deng, Chunyu Yuan•Sat Sep 30 2017

The proposed algorithm has advantages of high reconstruction performance for BOW, this storage method is high fidelity and low memory consumption; on the other hand, the algorithm yields highest accuracy in ECG beats classification; so this method is more suitable for large-scale heart beats data storage and classification.

6 0

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