AIMC Topic: Electrocardiography

Clear Filters Showing 21 to 30 of 1388 articles

Artificial intelligence capabilities in identifying atrial fibrillation using baseline sinus rhythm ECG : a systematic review.

Open heart
BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with adverse outcomes, often presenting paroxysmally. The lack of an efficient method to promptly detect paroxysmal AF and the absence of a unified screening approach necessita...

Universal Atrial Fibrillation Screening Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach in Rural Communities.

Journal of medical systems
Atrial fibrillation (AF) significantly contributes to the incidence of strokes. Screening for AF enhances its detection and effective management. However, universal AF screening in rural areas poses a challenge. This study evaluates the cost-effectiv...

Deep learning-driven contactless ECG in MRI via beat pilot tone for motion-resolved image reconstruction and heart rate monitoring.

Physics in medicine and biology
Electrocardiogram (ECG) is crucial for synchronizing cardiovascular magnetic resonance imaging (CMRI) acquisition with the cardiac cycle and for continuous heart rate monitoring during prolonged scans. However, conventional electrode-based ECG system...

A performance analysis of convolutional autoencoder modified WaveGAN architectures for realistic 12 lead electrocardiogram synthesis.

Scientific reports
The burgeoning necessity for copious and diverse electrocardiogram (ECG) datasets for deep learning applications in clinical diagnostics has been impeded by the confidential nature of patient data. Related works have shown the effectiveness of additi...

Improved non-invasive detection of sleep stages when combining skin sympathetic nerve activity and heart rate variability analysis with AI.

Scientific reports
Sleep is a cyclic physiological process that goes into different stages, and every stage has its' importance in the construction or recovery of physiological function. Sleep scoring is performed from polysomnography recordings which requires signals ...

Serial 12-Lead Electrocardiogram-Based Deep-Learning Model for Hospital Admission Prediction in Emergency Department Cardiac Presentations: Retrospective Cohort Study.

JMIR cardio
BACKGROUND: Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately disc...

Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy.

PloS one
Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically...

Interpretable deep learning for personalized energy expenditure prediction using ECG and acceleration signals in incremental exercise.

Scientific reports
Energy expenditure (EE) assessment is crucial in both sports science and health management. However, current EE prediction models often overlook individual differences and lack dynamic correlation analysis between multi-modal data and EE. Building up...

Feature extraction and intelligent diagnosis of ECG signals based on KANs and xLSTM.

Biosensors & bioelectronics
Cardiovascular disease (CVD) is the top cause of mortality globally, making it crucial to diagnose arrhythmias promptly and accurately for the early prevention and treatment of CVD. While numerous methods exist for detecting arrhythmias using ECG sig...

Panic Attack Prediction for Patients With Panic Disorder via Machine Learning and Wearable Electrocardiography Monitoring: Model Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Panic attack prediction remains a critical challenge in mental health care due to the high interindividual variability of physiological responses and the limitations of subjective psychological assessments.