AIMC Topic: Arrhythmias, Cardiac

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A Hybrid GCN-LSTM Model for Ventricular Arrhythmia Classification Based on ECG Pattern Similarity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Accurate differentiation between Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) is essential in the field of cardiology. Recent advancements in deep learning have facilitated automated arrhythmia recognition, surpassing traditional el...
Tachycardia, Ventricular Ventricular Fibrillation Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Algorithms Electrocardiography Neural Networks, Computer
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A CNN and Transformer Hybrid Network for Multi-Class Arrhythmia Detection from Photoplethysmography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Photoplethysmography (PPG)-based arrhythmia detection methods have gained attention with wearable technology, enabling early detection of undiagnosed arrhythmias. Existing methods excel in single arrhythmia detection but struggle with multiple arrhyt...
Humans Photoplethysmography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Neural Networks, Computer Algorithms
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Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Radiology. Cardiothoracic imaging Jun 1, 2024
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant ...
Phenotype Retrospective Studies Humans Male Middle Aged Magnetic Resonance Imaging Female Adult Magnetic Resonance Imaging, Cine Arrhythmias, Cardiac Mitral Valve Prolapse Unsupervised Machine Learning Registries
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AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Mathematical biosciences and engineering : MBE May 10, 2024
Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases...
Arrhythmias, Cardiac Neural Networks, Computer Algorithms Electrocardiography Signal Processing, Computer-Assisted Reproducibility of Results Humans
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Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

JAMA cardiology Apr 1, 2024
IMPORTANCE: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on re...
Arrhythmias, Cardiac Electrocardiography Long QT Syndrome Adult Cross-Sectional Studies Deep Learning Humans Male Genotype Female
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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Mathematical biosciences and engineering : MBE Mar 22, 2024
Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imba...
Algorithms Databases, Factual Sensitivity and Specificity Deep Learning Reproducibility of Results Humans Neural Networks, Computer Arrhythmias, Cardiac Electrocardiography Signal Processing, Computer-Assisted
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Letter to the Editor regarding How can artificial intelligence enhance the role of CT in arrhythmia management?

The British journal of radiology Feb 2, 2024
Tomography, X-Ray Computed Arrhythmias, Cardiac Humans Artificial Intelligence
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Deep Learning to Identify Undiagnosed AF Using ECGs in Sinus Rhythm-Should We Rewire Our Models?

JAMA cardiology Dec 1, 2023
Humans Arrhythmias, Cardiac Deep Learning Electrocardiography
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Memory Classifiers for Robust ECG Classification against Physiological Noise.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural ne...
Electrocardiography Arrhythmias, Cardiac Machine Learning Humans Algorithms Neural Networks, Computer
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Classification of Continuous ECG Segments - Performance Analysis of a Deep Learning Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. It is a complex and non-linear signal, which is the first option to preliminary identify specific pathologies/conditions (e.g., arrhythmias)...
Arrhythmias, Cardiac Deep Learning Electrocardiography Neural Networks, Computer Machine Learning Humans
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