Myocardial Infarction

MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields

Recent research in medical image analysis with deep learning almost exclusively focuses on grid- or voxel-based data representations. We challenge this common choice by introducing MedFuncta, a modality-agnostic continuous data representation based on neural fields. We demonstrate how to scale neural fields from single instances to large datasets by exploiting redundancy in medical signals and b...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.

Comparing Deep Neural Network for Multi-Label ECG Diagnosis From Scanned ECG

Automated ECG diagnosis has seen significant advancements with deep learning techniques, but real-world applications still face challenges when deal...

An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation

Large language models (LLMs) are revolutionizing healthcare by improving diagnosis, patient care, and decision support through interactive communica...

Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes

Acute Coronary Syndrome (ACS) is a life-threatening cardiovascular condition where early and accurate diagnosis is critical for effective treatment ...

ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis

We present ECG-Expert-QA, a comprehensive multimodal dataset for evaluating diagnostic capabilities in electrocardiogram (ECG) interpretation. It co...

DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection

This article introduces DT4ECG, an innovative dual-task learning framework for Electrocardiogram (ECG)-based human identity recognition and activity...

Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model

Electrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECG ofte...

FADE: Forecasting for Anomaly Detection on ECG

Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes...

Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials

Clinical trials remain critical in cardiac drug development but face high failure rates due to efficacy limitations and safety risks, incurring subs...

Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection

Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing cardiovascular conditions, yet anomaly detection in ECG signals remains challe...

DE-PADA: Personalized Augmentation and Domain Adaptation for ECG Biometrics Across Physiological States

Electrocardiogram (ECG)-based biometrics offer a promising method for user identification, combining intrinsic liveness detection with morphological...

Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms

Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. ...

Deep learning model for ECG reconstruction reveals the information content of ECG leads

This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs). The model was...

Convolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification

Machine learning has revolutionized biomedical signal analysis, particularly in electrocardiogram (ECG) classification. While convolutional neural n...

DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits

DCentNet is a novel decentralized multistage signal classification approach designed for biomedical data from IoT wearable sensors, integrating earl...

High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2

Electrocardiogram (ECG) interpretation is a cornerstone of cardiac diagnostics. This paper explores a practical approach to enhance ECG image interp...

[Application Status of Machine Learning in Assisted Diagnosis Techniques of Cardiovascular Diseases].

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processin...

DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information

Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac ...