Myocardial Infarction

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study

Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfunction (LVSD), but their head-to-head agreement and performance have not been independently compared within the same cohort. To compare the performance of published AI-ECG models for LVSD detection in a standardized external cohort and evaluate the field’s trans...

Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction

Despite its broadening indications, the implantable cardiac monitor (ICM) records a narrow, nonstandard electrocardiogram (ECG) signal which precludes morphological and functional assessments or the application of 12-lead ECG models. We hypothesize that deep learning can be used to reconstruct 12-lead ECG from a single ICM lead for continuously assessing clinical endpoints outside of rhythm detect...

Comparison of Time- and Frequency-Domain Methods for Assessing Brain Compliance in Brain-Injured Patients Monitored With Intraparenchymal Intracranial Pressure Sensors

Intracranial pressure (ICP) monitoring is commonly used in neuro-intensive care, but its utility may be limited by a suboptimal use. The brain pressur...

Artificial intelligence-driven ECG biomarkers for screening of large pericardial effusion

Pericardial effusion can progress to life-threatening cardiac tamponade when large or rapidly accumulating, yet early diagnosis is frequently delayed ...

An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare Study

Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic...

Advancing Cardiovascular Disease Diagnosis with an Interpretable and Responsible AI Framework

Cardiovascular disease (CVD) remains a leading global health threat, responsible for one in five deaths worldwide. Early detection is critical to miti...

Multitask Artificial Intelligence–Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs

Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing ...

Early Detection of Cardiovascular Disease Risk Using Multi-Parameter Biomarker Analysis and Machine Learning: A Prospective Cohort Study

Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed condit...

Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.

Atrial fibrillation (AF) is a heart condition widely recognized as a significant risk factor for stroke. Left atrial (LA) volume variation has been id...

Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.

Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and q...

Transfer learning in ECG diagnosis: Is it effective?

The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to t...

A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.

Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is du...

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

OBJECTIVE: Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram ...

ECG-guided individual identification via PPG

Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive ...

MobileNetV2: A lightweight classification model for home-based sleep apnea screening

This study proposes a novel lightweight neural network model leveraging features extracted from electrocardiogram (ECG) and respiratory signals for ...

[A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features].

During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ab...

Mamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography

Study Objectives: We investigate Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non...

Compact Neural Network Algorithm for Electrocardiogram Classification

In this paper, we present a high-performance, compact electrocardiogram (ECG)-based system for automatic classification of arrhythmias, integrating ...

Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration

The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and incon...

Evaluating the Efficacy of Vectocardiographic and ECG Parameters for Efficient Tertiary Cardiology Care Allocation Using Decision Tree Analysis

Use real word data to evaluate the performance of the electrocardiographic markers of GEH as features in a machine learning model with Standard ECG ...