Arrhythmias

Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.

Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict (1) in utero or neonatal death, (2) high-acuity...

Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pa...

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach.

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models...

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia...

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening.

OBJECTIVE: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore,...

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this stud...

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hi...

Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that D...

Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification.

Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years,...

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional s...

Expert-level sleep staging using an electrocardiography-only feed-forward neural network.

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and underst...

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This stu...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use ...

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs.

BACKGROUND: Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to d...

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical s...

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professio...

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in ...

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF...

Classification Method of ECG Signals Based on RANet.

BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arr...

Apnoea detection using ECG signal based on machine learning classifiers and its performances.

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain disea...