[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 …
[HTML][HTML] Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future
F Yasmin, SMI Shah, A Naeem… - Reviews in …, 2021 - imrpress.com
Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as
Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of …
Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of …
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 …
Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model
Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs).
They are also being used to develop computer-assisted methods for heart disease …
They are also being used to develop computer-assisted methods for heart disease …
Computational diagnostic techniques for electrocardiogram signal analysis
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina,
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
Machine learning in arrhythmia and electrophysiology
Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
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 …
[PDF][PDF] Fusion based feature extraction analysis of ECG signal interpretation–a systematic approach
T Vijayakumar, R Vinothkanna… - Journal of Artificial …, 2021 - researchgate.net
Our human heart is classified into four sections called the left side and right side of the
atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is …
atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is …
Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery
Background: The ECG remains the most widely used diagnostic test for characterization of
cardiac structure and electrical activity. We hypothesized that parallel advances in …
cardiac structure and electrical activity. We hypothesized that parallel advances in …