Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Showing 169-189 of 6,871 articles
Transformer-based heart language model with electrocardiogram annotations.

This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillati...

An arrhythmia classification using a deep learning and optimisation-based methodology.

The work proposes a methodology for five different classes of ECG signals. The methodology utilises ...

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachyc...

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of ...

AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the ...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring.

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data t...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate int...

Advancements and challenges in high-sensitivity cardiac troponin assays: diagnostic, pathophysiological, and clinical perspectives.

Although significant progress has been made in recent years, some important questions remain regardi...

Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grad...

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 wave...

Statin use and longitudinal bone marrow lesion burden: analysis of knees without osteoarthritis from the Osteoarthritis Initiative study.

OBJECTIVES: Knee subchondral bone marrow lesions (BMLs) are one of the hallmark features of structur...

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 lo...

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

BACKGROUND: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood ...

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 biosign...

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