Arrhythmias

[Automatic detection model of hypertrophic cardiomyopathy based on deep convolutional neural network].

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of sing...

[Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss function].

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is buil...

Fighting against sudden cardiac death: need for a paradigm shift-Adding near-term prevention and pre-emptive action to long-term prevention.

More than 40 years after the first implantable cardioverter-defibrillator (ICD) implantation, sudden cardiac death (SCD) still accounts for more than ...

Deep learning-based ballistocardiography reconstruction algorithm on the optical fiber sensor.

Ballistocardiography (BCG) is a vibration signal related to cardiac activity, which can be obtained in a non-invasive way by optical fiber sensors. In...

TF-Unet:An automatic cardiac MRI image segmentation method.

Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different ty...

Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof...

Management of arrhythmias: the increasing role of artificial intelligence, genetics and cardiac resyncronization.

[Design and Implementation of Software Platform for AI-ECG Algorithm Research].

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classificati...

ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach.

Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according...

An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels.

Cardiovascular disease (CVD) is a serial of diseases with global leading causes of death. Electrocardiogram (ECG) is the most commonly used basis for ...

Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms.

Low-cost wearables with capability to record electrocardiograms (ECG) are becoming increasingly available. These wearables typically acquire single-le...

Dual Attention Convolutional Neural Network Based on Adaptive Parametric ReLU for Denoising ECG Signals with Strong Noise.

Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoisin...

Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network.

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation a...

Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification.

The automatic arrhythmia classification system has made a significant contribution to reducing the mortality rate of cardiovascular diseases. Although...

Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms.

ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person ...

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) pr...

A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion.

Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for object...

A deep learning algorithm for detecting acute myocardial infarction.

BACKGROUND: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram...

Artificial intelligence in the diagnosis and management of arrhythmias.

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep...

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricul...