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 …
Computer-interpreted electrocardiograms: benefits and limitations
J Schläpfer, HJ Wellens - Journal of the American College of Cardiology, 2017 - jacc.org
Computerized interpretation of the electrocardiogram (CIE) was introduced to improve the
correct interpretation of the electrocardiogram (ECG), facilitating health care decision …
correct interpretation of the electrocardiogram (ECG), facilitating health care decision …
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 …
Cardiologist-level arrhythmia detection with convolutional neural networks
We develop an algorithm which exceeds the performance of board certified cardiologists in
detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single …
detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single …
Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation
Importance Millions of clinicians rely daily on automated preliminary electrocardiogram
(ECG) interpretation. Critical comparisons of machine learning–based automated analysis …
(ECG) interpretation. Critical comparisons of machine learning–based automated analysis …
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …
critical to timely medical treatment to save patients' lives. Routine use of the …
The computerized ECG: friend and foe
H Smulyan - The American journal of medicine, 2019 - Elsevier
Computerized interpretation of the electrocardiogram (ECG) began in the 1950s when
conversion of its analog signal to digital form became available. Since then, automatic …
conversion of its analog signal to digital form became available. Since then, automatic …
Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …
apply artificial intelligence and other advanced analytical tools to transform health care …
Electrocardiographic left atrial abnormality and stroke subtype in the atherosclerosis risk in communities study
Objective The aim of this study was to assess the relationship between abnormally
increased P‐wave terminal force in lead V1, an electrocardiographic (ECG) marker of left …
increased P‐wave terminal force in lead V1, an electrocardiographic (ECG) marker of left …
[HTML][HTML] A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: algorithm development
Background The detection of dyskalemias—hypokalemia and hyperkalemia—currently
depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia …
depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia …