Myocardial Infarction

PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasing risks of obesity and deconditioning due to inactivity. Conversely, manual wheelchair users, who propel the wheelchair by pushing the wheelchair's handrims, often face upper extremity injuries from repetitive motions. These challenges underscore the...

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-crafted features and deep learning via transformed images of ECG beats. The dataset underwent preprocessing steps, including downsampling, filtering, and normalization, to ensure consistency and relevance for subsequent analysis. In the first approac...

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...

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...

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...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment pr...

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...

A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization

Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary blood flow and treats coronary artery disease. Alt...

Beyond 1D: Vision Transformers and Multichannel Signal Images for PPG-to-ECG Reconstruction

Reconstructing ECG from PPG is a promising yet challenging task. While recent advancements in generative models have significantly improved ECG reco...

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in indi...