Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review
Heart disease is one of the leading causes of mortality throughout the world. Among the
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …
Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis
Cardiovascular diseases are one of the leading global causes of mortality. Currently,
clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) …
clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) …
PTB-XL+, a comprehensive electrocardiographic feature dataset
Abstract Machine learning (ML) methods for the analysis of electrocardiography (ECG) data
are gaining importance, substantially supported by the release of large public datasets …
are gaining importance, substantially supported by the release of large public datasets …
Opening the black box: interpretability of machine learning algorithms in electrocardiography
Recent studies have suggested that cardiac abnormalities can be detected from the
electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL …
electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL …
Machine learning-based heart disease diagnosis: A systematic literature review
MM Ahsan, Z Siddique - Artificial Intelligence in Medicine, 2022 - Elsevier
Heart disease is one of the significant challenges in today's world and one of the leading
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
Meta-analysis of the performance of AI-driven ECG interpretation in the diagnosis of valvular heart diseases
Valvular heart diseases (VHDs) significantly impact morbidity and mortality rates worldwide.
Early diagnosis improves patient outcomes. Artificial intelligence (AI) applied to …
Early diagnosis improves patient outcomes. Artificial intelligence (AI) applied to …
Computer modeling of the heart for ECG interpretation—a review
Computer modeling of the electrophysiology of the heart has undergone significant
progress. A healthy heart can be modeled starting from the ion channels via the spread of a …
progress. A healthy heart can be modeled starting from the ion channels via the spread of a …
Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications
Background The application of artificial intelligence to interpret the electrocardiogram (ECG)
has predominantly included the use of knowledge engineered rule-based algorithms which …
has predominantly included the use of knowledge engineered rule-based algorithms which …
[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 …
Electrocardiogram monitoring and interpretation: from traditional machine learning to deep learning, and their combination
S Parvaneh, J Rubin - 2018 Computing in Cardiology …, 2018 - ieeexplore.ieee.org
Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic
burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The …
burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The …