ECG Classification

ECG classification aims to automatically diagnose cardiovascular conditions from electrocardiogram (ECG) signals, improving diagnostic speed and accuracy. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating techniques to handle noise and artifacts, and improve robustness. These advancements hold significant promise for improving the efficiency and accessibility of cardiac diagnosis, particularly in resource-constrained settings or for early detection of life-threatening conditions like myocardial infarction.

Papers

April 7, 2022

Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Muhammad Uzair Zahid, Serkan Kiranyaz, Moncef Gabbouj
Electrocardiogram Classification ECG Classification Arrhythmia Detection MIT BIH Arrhythmia Feature Splatting

March 14, 2022

Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
Jiahao Shao, Shijia Geng, Zhaoji Fu, Weilun Xu, Tong Liu, Shenda Hong
Adversarial Attack ECG Classification Traditional Adversarial Adversarial Distillation White Box Adversarial Attack

February 8, 2022

Effective classification of ECG signals using enhanced convolutional neural network in IOT
Ahmad M. Karim
IOT Technology ECG Signal ECG Classification Electrocardiogram Representation Effective Classifier ECG Monitoring System

January 25, 2022

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao
Optimal Transport Data Imbalance ECG Classification ECG Datasets Heart Disease Detection

December 11, 2021

Towards automated optimisation of residual convolutional neural networks for electrocardiogram classification
Zeineb Fki, Boudour Ammar, Mounir Ben Ayed
Convolutional Neural Network Deep Learning Algorithm Optimization Process Electrocardiogram Analysis ECG Classification Electrocardiogram Feature Electrocardiogram Dataset

December 1, 2021

Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Zhibin Zhao, Darcy Murphy, Hugh Gifford, Stefan Williams, Annie Darlington, Samuel D. Relton, Hui Fang, David C. Wong
General Analysis Deep Feature Multiclass Classification ResNet Based Lead ECG ECG Classification

ECG Classification

Papers

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training

Effective classification of ECG signals using enhanced convolutional neural network in IOT

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

Towards automated optimisation of residual convolutional neural networks for electrocardiogram classification

Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs