4.2 Article

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

Related references

Note: Only part of the references are listed.
Article Engineering, Biomedical

Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

Jagdeep Rahul et al.

Biocybernetics and Biomedical Engineering (2022)

Article Biology

Self-supervised representation learning from 12-lead ECG data

Temesgen Mehari et al.

Summary: This study provides the first comprehensive assessment of self-supervised representation learning from clinical 12-lead ECG data, demonstrating the advantages in extracting discriminative representations and finetuning them for downstream tasks.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Interdisciplinary Applications

The influence of atrial flutter in automated detection of atrial arrhythmias-are we ready to go into clinical practice??

Viktor Domazetoski et al.

Summary: This study investigates the impact of atrial flutter in the classification of atrial arrhythmias and suggests the use of a subject-based split to avoid within-subject correlation. The results show that the XGBoost model performs the best across different datasets, but there are variations in model performance among datasets. Comparisons between random split and patient split reveal significant differences in datasets with a large number of samples per patient. Inter-dataset evaluations also demonstrate lower scores compared to intra-dataset evaluations, indicating the need for improved generalization of the models.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Article Computer Science, Information Systems

A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation

Majid Sepahvand et al.

Summary: This paper proposes a method to bridge the gap between arrhythmia classification models using multi-lead ECG signals and those using single-lead ECG signals through knowledge distillation. The results show that the method successfully compresses the model size while maintaining a high level of accuracy.

INFORMATION SCIENCES (2022)

Article Biophysics

A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography

Jingsu Kang et al.

Summary: This study has made positive progress in handling varying-dimensional electrocardiography, improving model performance through deep learning models and the 'lead-wise' mechanism, laying a solid foundation for future research.

PHYSIOLOGICAL MEASUREMENT (2022)

Article Mathematics

A New 12-Lead ECG Signals Fusion Method Using Evolutionary CNN Trees for Arrhythmia Detection

Maytham N. Meqdad et al.

Summary: This paper proposes a method to fuse electrocardiogram signals using time-frequency transform and structural learning, and optimize the features through genetic programming. The proposed method achieves a high accuracy of 97.60% in diagnosing arrhythmias on the Chapman dataset.

MATHEMATICS (2022)

Article Computer Science, Information Systems

Meta Structural Learning Algorithm With Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multisession ECG

Maytham N. Meqdad et al.

Summary: This article proposes a new interpretable meta structural learning algorithm for the detection of arrhythmia in electrocardiogram signals. By collaborating between models and transferring knowledge, the algorithm maintains generalization when dealing with unseen samples. To improve interpretability, CNN models are encoded as evolutionary trees using genetic programming algorithms. Experimental results show that the proposed model achieves high accuracy and performs competitively compared to other models based on big deep models.

IEEE ACCESS (2022)

Article Geochemistry & Geophysics

CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

Qichao Liu et al.

Summary: This paper proposes a heterogeneous deep network called CEGCN, which integrates CNN and GCN branches for feature learning on different scales of image regions to generate complementary spectral-spatial features. By integrating the graph encoding process into the network and learning edge weights from training data, it promotes node feature learning and makes the graph more adaptive to HSI content.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Editorial Material Chemistry, Analytical

Special Issue: ECG Monitoring System

Florent Baty

SENSORS (2021)

Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Medicine, General & Internal

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

Xiaoling Liang et al.

Summary: A 3D deep learning method was proposed for rapid diagnosis of COVID-19 using a COVID-19 graph in GCN, achieving high accuracy, sensitivity, and specificity.

FRONTIERS IN MEDICINE (2021)

Article Computer Science, Artificial Intelligence

Electrocardiogram soft computing using hybrid deep learning CNN-ELM

Shuren Zhou et al.

APPLIED SOFT COMPUTING (2020)

Article Cardiac & Cardiovascular Systems

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram

Zhi Li et al.

JOURNAL OF ELECTROCARDIOLOGY (2020)

Review Chemistry, Analytical

Deep Learning in Physiological Signal Data: A Survey

Beanbonyka Rim et al.

SENSORS (2020)

Review Medicine, General & Internal

Classic and Novel Biomarkers as Potential Predictors of Ventricular Arrhythmias and Sudden Cardiac Death

Zornitsa Shomanova et al.

JOURNAL OF CLINICAL MEDICINE (2020)

Article Multidisciplinary Sciences

A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients

Jianwei Zheng et al.

SCIENTIFIC DATA (2020)

Review Cardiac & Cardiovascular Systems

Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy

Helder Pereira et al.

EUROPACE (2020)

Article Emergency Medicine

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Joon-myoung Kwon et al.

SCANDINAVIAN JOURNAL OF TRAUMA RESUSCITATION & EMERGENCY MEDICINE (2020)

Article Computer Science, Interdisciplinary Applications

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,0 00 individual subject ECG records

Ozal Yildirim et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Education & Educational Research

Effectiveness of blended learning versus lectures alone on ECG analysis and interpretation by medical students

Charle Andre Viljoen et al.

BMC MEDICAL EDUCATION (2020)

Article Multidisciplinary Sciences

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Younghoon Cho et al.

SCIENTIFIC REPORTS (2020)

Article Multidisciplinary Sciences

Towards better heartbeat segmentation with deep learning classification

Pedro Silva et al.

SCIENTIFIC REPORTS (2020)

Review Chemistry, Analytical

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Liping Xie et al.

SENSORS (2020)

Article Computer Science, Interdisciplinary Applications

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

M. V. Valueva et al.

MATHEMATICS AND COMPUTERS IN SIMULATION (2020)

Article Computer Science, Information Systems

Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks

Abdelrahman M. Shaker et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Classification of myocardial infarction with multi-lead ECG signals and deep CNN

Ulas Baran Baloglu et al.

PATTERN RECOGNITION LETTERS (2019)

Article Computer Science, Information Systems

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks

Sean Shensheng Xu et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Article Health Care Sciences & Services

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Junli Gao et al.

JOURNAL OF HEALTHCARE ENGINEERING (2019)

Article Biology

Arrhythmia detection using deep convolutional neural network with long duration ECG signals

Ozal Yildirim et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2018)

Review Radiology, Nuclear Medicine & Medical Imaging

Convolutional neural networks: an overview and application in radiology

Rikiya Yamashita et al.

INSIGHTS INTO IMAGING (2018)

Article Biology

A deep convolutional neural network model to classify heartbeats

U. Rajendra Acharya et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2017)

Editorial Material Mathematical & Computational Biology

Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling

Nader Salari et al.

THEORETICAL BIOLOGY AND MEDICAL MODELLING (2013)

Article Computer Science, Theory & Methods

Mutual Information Analysis: a Comprehensive Study

Lejla Batina et al.

JOURNAL OF CRYPTOLOGY (2011)