Arrhythmias

An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, is marking a pivotal moment in epidemiology and public health research. By harnessing the capabilities of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract useful epidemiological informa...

Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

We present Heartcare Suite, a multimodal comprehensive framework for finegrained electrocardiogram (ECG) understanding. It comprises three key components: (i) Heartcare-220K, a high-quality, structured, and comprehensive multimodal ECG dataset covering essential tasks such as disease diagnosis, waveform morphology analysis, and rhythm interpretation. (ii) Heartcare-Bench, a systematic and multi-...

Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning

This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-...

Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden card...

Uncertainty-Aware Multi-view Arrhythmia Classification from ECG

We propose a deep neural architecture that performs uncertainty-aware multi-view classification of arrhythmia from ECG. Our method learns two differ...

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, exist...

Full duty cycle laser ablation plasma spectrometry (LAPS) combining a hollow-core toroidal coil with artificial intelligence classification.

The use of a hollow-core toroidal coil (HTC) as an induction detector allows for the analysis of ions in an expanding plasma released through laser ab...

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could signif...

From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.

Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging ...

Cardiac Phase Estimation Using Deep Learning Analysis of Pulsed-Mode Projections: Toward Autonomous Cardiac CT Imaging.

Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase an...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This stu...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing conditions such as left bundle branch block. To address the...

QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.

We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. AF, a major cardiac arrhythmi...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascular conditions. Detecting these anomalies early thr...

Portable ECG and PCG wireless acquisition system and multiscale CNN feature fusion Bi-LSTM network for coronary artery disease diagnosis.

Coronary artery disease (CAD) is a major cause of mortality, especially among aging populations, making timely and accurate diagnosis essential. In th...

Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. T...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artific...

Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis.

Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography...

Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification

The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in...

GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.

OBJECTIVES: An image-based ECG dataset incorporating visual imperfections common to paper-based ECGs, which are typically scanned or photographed into...