Practical lessons on 12-lead ECG classification: Meta-analysis of methods from PhysioNet/computing in cardiology challenge 2020
Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide.
Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac …
Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac …
Robust label and feature space co-learning for multi-label classification
Z Liu, C Tang, SE Abhadiomhen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Multi-label classification remains a challenging task for high-dimensional data samples and
their labels both increase the complexity of training models. In this paper, we propose a …
their labels both increase the complexity of training models. In this paper, we propose a …
HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons
of death in the world. The timely, accurate, and effective prediction of heart diseases is …
of death in the world. The timely, accurate, and effective prediction of heart diseases is …
Potential of rule-based methods and deep learning architectures for ECG diagnostics
G Bortolan, I Christov, I Simova - Diagnostics, 2021 - mdpi.com
The main objective of this study is to propose relatively simple techniques for the automatic
diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and …
diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and …
Automatic Classification of Cardiac Arrhythmias using Deep Learning Techniques: A Systematic Review
F Vásquez-Iturralde, M Flores-Calero… - IEEE …, 2024 - ieeexplore.ieee.org
Cardiac arrhythmias are one of the main causes of death worldwide; therefore, early
detection is essential to save the lives of patients who suffer from them and to reduce the …
detection is essential to save the lives of patients who suffer from them and to reduce the …
Introduction and comparison of novel decentral learning schemes with multiple data pools for privacy-preserving ECG classification
Artificial intelligence and machine learning have led to prominent and spectacular
innovations in various scenarios. Application in medicine, however, can be challenging due …
innovations in various scenarios. Application in medicine, however, can be challenging due …
Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification
Objective. The availability of online electrocardiogram (ECG) repositories can aid
researchers in developing automated cardiac abnormality diagnostic systems. Using such …
researchers in developing automated cardiac abnormality diagnostic systems. Using such …
A systematic survey of data augmentation of ECG signals for AI applications
AI techniques have recently been put under the spotlight for analyzing electrocardiograms
(ECGs). However, the performance of AI-based models relies on the accumulation of large …
(ECGs). However, the performance of AI-based models relies on the accumulation of large …
Exploring Best Practices for ECG Signal Processing in Machine Learning
In this work we search for best practices in pre-processing of Electrocardiogram (ECG)
signals in order to train better classifiers for the diagnosis of heart conditions. State of the art …
signals in order to train better classifiers for the diagnosis of heart conditions. State of the art …
[HTML][HTML] Machine learning models for automated interpretation of 12-lead electrocardiographic signals: a narrative review of techniques, challenges, achievements …
Methods: We searched 10 indexing databases, for original research in English, referring to
the application of ML/DL techniques in 12-lead, raw ECG signal analysis. The retrieved titles …
the application of ML/DL techniques in 12-lead, raw ECG signal analysis. The retrieved titles …