ECG Classification
32 papers with code • 4 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in ECG Classification
Datasets
UCR Time Series Classification Archive
PhysioNet Challenge 2021
PhysioNet Challenge 2020
Most implemented papers
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
huckiyang/Voice2Series-Reprogramming
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Learning to classify time series with limited data is a practical yet challenging problem.
Deep Learning for ECG Classification
ismorphism/DeepECG
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Journal of Physics: Conference Series 2017
The importance of ECG classification is very high now due to many current medical applications where this problem can be stated.
Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings
Seb-Good/deep_ecg
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Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:83-101 2018
Access to electronic health record (EHR) data has motivated computational advances in medical research.
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
ydup/Anomaly-Detection-in-Time-Series-with-Triadic-Motif-Fields
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Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models.
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
hsd1503/ENCASE
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2017 Computing in Cardiology (CinC) 2017
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification.
Automatic diagnosis of the 12-lead ECG using a deep neural network
antonior92/automatic-ecg-diagnosis
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The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models.
Diffeomorphic Temporal Alignment Nets
BGU-CS-VIL/dtan
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NeurIPS 2019
In a single-class case, the method is unsupervised: the ground-truth alignments are unknown.
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length
SarielMa/ICMLA2020_12-lead-ECG
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Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification, a life-critical application.
ECG beats classification via online sparse dictionary and time pyramid matching
Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and promotes the accuracy of the ECG classification system.
