Arrhythmias

Multi-modal dataset creation for federated learning with DICOM-structured reports.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance...

Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.

Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences feature quality and diagnostic accuracy. Traditional methods typically rely on fixed wavelet bases chosen...

Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.

The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardiology community. The computer...

A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and bre...

Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.

BACKGROUND: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged a...

tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for moni...

A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy.

INTRODUCTION: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in younger individuals. Accurate diagnosis is crucial for m...

Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate ...

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, c...

Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestat...

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of...

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

OBJECTIVE: In Long-Term Monitoring (LTM), noise significantly impacts the quality of the electrocardiogram (ECG), posing challenges for accurate diagn...

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including th...

Residual-attention deep learning model for atrial fibrillation detection from Holter recordings.

BACKGROUND: Detecting subtle patterns of atrial fibrillation (AF) and irregularities in Holter recordings is intricate and unscalable if done manually...

Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron.

Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most diffic...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators a...

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks.

This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that balance high classification performance with robust pr...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-a...

Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.

We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R...

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely availab...