A survey of heart anomaly detection using ambulatory electrocardiogram (ECG)
H Li, P Boulanger - Sensors, 2020 - mdpi.com
Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated
17.9 million people die from CVDs each year, representing 31% of all global deaths. Most …
17.9 million people die from CVDs each year, representing 31% of all global deaths. Most …
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals
Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an
electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart …
electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart …
Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
J Lai, H Tan, J Wang, L Ji, J Guo, B Han, Y Shi… - Nature …, 2023 - nature.com
Cardiovascular disease is a major global public health problem, and intelligent diagnostic
approaches play an increasingly important role in the analysis of electrocardiograms …
approaches play an increasingly important role in the analysis of electrocardiograms …
Congestive heart failure detection using random forest classifier
Background and objectives Automatic electrocardiogram (ECG) heartbeat classification is
substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of …
substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of …
Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review
Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious
tasks (eg, Holter ECG monitored in Intensive Care Units) or in prompt detection of …
tasks (eg, Holter ECG monitored in Intensive Care Units) or in prompt detection of …
A multi-view multi-scale neural network for multi-label ECG classification
The 12-lead electrocardiogram (ECG) is a common method used to diagnose
cardiovascular diseases. Recently, ECG classification using deep neural networks has been …
cardiovascular diseases. Recently, ECG classification using deep neural networks has been …
Optimization of ECG classification by means of feature selection
This study tackles the ECG classification problem by means of a methodology, which is able
to enhance classification performance while simultaneously reducing the computational …
to enhance classification performance while simultaneously reducing the computational …
Generative adversarial network with transformer generator for boosting ECG classification
Y Xia, Y Xu, P Chen, J Zhang, Y Zhang - Biomedical Signal Processing and …, 2023 - Elsevier
Arrhythmia is an important group of cardiovascular diseases, which can suddenly attack and
cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram …
cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram …
Arrhythmia recognition and classification using combined parametric and visual pattern features of ECG morphology
H Yang, Z Wei - IEEE Access, 2020 - ieeexplore.ieee.org
ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias
classification solutions with various ECG features have been reported in literature. In this …
classification solutions with various ECG features have been reported in literature. In this …
ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical features
MR Homaeinezhad, SA Atyabi, E Tavakkoli… - Expert Systems with …, 2012 - Elsevier
In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification
solution, is introduced. Proposed method consists of structurally diverse classifiers with a …
solution, is introduced. Proposed method consists of structurally diverse classifiers with a …