Arrhythmias

Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals.

Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the emergency setting. Machine learning is increasingly being explored for automated computer-aided ECG diagnosis of cardiovascular diseases. In this study, we have developed DenseNet and CNN models for the classification of...

Automatic Evaluation of Motor Rehabilitation Exercises Based on Deep Mixture Density Neural Networks.

An automatic assessment system for physical telerehabilitation could reduce the time and cost of treatments. But such assessment involves stochastic uncertainties, nonlinearities, and complexities of human movement. Probabilistic models and deep structures are two categories that could, respectively, address the stochastic uncertainty and complexity of motion data. In the proposed Deep Mixture Den...

Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmia diseases. Thromboembolic prophylaxis plays an essential role in AF t...

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable...

Effects of data and entity ablation on multitask learning models for biomedical entity recognition.

MOTIVATION: Training domain-specific named entity recognition (NER) models requires high quality hand curated gold standard datasets which are time-co...

Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.

BACKGROUND: Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead el...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiol...

Machine learning-based heart disease diagnosis: A systematic literature review.

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of mach...

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach.

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstrati...

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academ...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablat...

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-t...

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are h...

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.

PURPOSE: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG)...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract ta...

An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net).

Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects...

MCG-Net: End-to-End Fine-Grained Delineation and Diagnostic Classification of Cardiac Events From Magnetocardiographs.

In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating convolutional neural network (CNN) with transform...

Update on risk factors and biomarkers of sudden unexplained cardiac death.

Sudden cardiac death (SCD) accounts for approximately 15%-20% of all deaths worldwide, the causes of which are mainly structural heart diseases. Howev...

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in th...

The Identification of ECG Signals Using WT-UKF and IPSO-SVM.

The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) s...