Deep learning and the electrocardiogram: review of the current state-of-the-art
In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
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 …
Machine learning in the electrocardiogram
The electrocardiogram is the most widely used diagnostic tool that records the electrical
activity of the heart and, therefore, its use for identifying markers for early diagnosis and …
activity of the heart and, therefore, its use for identifying markers for early diagnosis and …
Artificial intelligence for the electrocardiogram
A Mincholé, B Rodriguez - Nature medicine, 2019 - nature.com
Artificial intelligence for the electrocardiogram | Nature Medicine Skip to main content Thank
you for visiting nature.com. You are using a browser version with limited support for CSS. To …
you for visiting nature.com. You are using a browser version with limited support for CSS. To …
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 …
Comprehensive electrocardiographic diagnosis based on deep learning
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery
disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left …
disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left …
Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification
Background Electrocardiogram (ECG) signal classification plays a critical role in the
automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be …
automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be …
Deep learning for ECG analysis: Benchmarks and insights from PTB-XL
Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its
interpretation is increasingly supported by algorithms. The progress in the field of automatic …
interpretation is increasingly supported by algorithms. The progress in the field of automatic …
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and
standardized test, is an example of the ongoing transformative effect of AI on cardiovascular …
standardized test, is an example of the ongoing transformative effect of AI on cardiovascular …
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Deep learning-based tools may annotate and interpret medical data more quickly,
consistently, and accurately than medical doctors. However, as medical doctors are …
consistently, and accurately than medical doctors. However, as medical doctors are …