Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
[HTML][HTML] Comprehensive survey of computational ECG analysis: Databases, methods and applications
E Merdjanovska, A Rashkovska - Expert Systems with Applications, 2022 - Elsevier
Electrocardiogram (ECG) recordings are indicative for the state of the human heart.
Automatic analysis of these recordings can be performed using various computational …
Automatic analysis of these recordings can be performed using various computational …
Self-supervised contrastive pre-training for time series via time-frequency consistency
Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
[HTML][HTML] PTB-XL, a large publicly available electrocardiography dataset
P Wagner, N Strodthoff, RD Bousseljot, D Kreiseler… - Scientific data, 2020 - nature.com
Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases
which is increasingly supported by algorithms based on machine learning. Major obstacles …
which is increasingly supported by algorithms based on machine learning. Major obstacles …
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
Automatic diagnosis of the 12-lead ECG using a deep neural network
AH Ribeiro, MH Ribeiro, GMM Paixão… - Nature …, 2020 - nature.com
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the
accuracy of existing models. Deep Neural Networks (DNNs) are models composed of …
accuracy of existing models. Deep Neural Networks (DNNs) are models composed of …
Will two do? Varying dimensions in electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021
The PhysioNet/Computing in Cardiology Challenge 2021 focused on the identification of
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
F Di Martino, F Delmastro - Artificial Intelligence Review, 2023 - Springer
Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …
healthcare applications, both clinical and remote, but the best performing AI systems are …
Heart murmur detection from phonocardiogram recordings: The george b. moody physionet challenge 2022
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify
patients with heart murmurs, who may need follow-up diagnostic screening and treatment for …
patients with heart murmurs, who may need follow-up diagnostic screening and treatment for …
Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference
Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains
difficult and AF-related complications cause unacceptable morbidity and mortality even on …
difficult and AF-related complications cause unacceptable morbidity and mortality even on …