AIMC Topic: Electrocardiography

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Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Pediatric cardiology
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its applicat...

Ultra-Low Power Analog Folded Neural Network for Cardiovascular Health Monitoring.

IEEE journal of biomedical and health informatics
Wearable sensors are increasingly used for continuous health monitoring, but their small size limits battery capacity, affecting user experience and monitoring capabilities. To overcome this, we introduce an ultra-low power analog Folded Neural Netwo...

GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.

BMJ health & care informatics
OBJECTIVES: An image-based ECG dataset incorporating visual imperfections common to paper-based ECGs, which are typically scanned or photographed into electronic health records, could facilitate clinically useful artificial intelligence (AI)-ECG algo...

Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.

Annals of family medicine
PURPOSE: Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools ...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.

European heart journal
BACKGROUND AND AIMS: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of...

IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.

Scientific reports
Heart disease is one of the major causes of death worldwide, and the traditional diagnostic procedures typically cause delays in treatment, particularly in low-resource regions. In this article, we propose a novel IoT-based Quantum Kernel-Enhanced Sa...

Optimized deep residual networks for early detection of myocardial infarction from ECG signals.

BMC cardiovascular disorders
Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to ...

Telecardiology unleashed: probing the depths of effectiveness in remote monitoring and telemedicine applications for acute cardiac conditions.

European heart journal. Acute cardiovascular care
Telecardiology has emerged as a promising approach in acute cardiac care through advancements in digital health technologies. This review explores the current evidence of telemedicine applications in acute coronary syndrome, arrhythmias, and acute he...

Challenging Black-Box Models: Interpretable Explanations for ECG Classification.

Studies in health technology and informatics
Deep learning methods achieve high performance, while often lacking explainability, hindering application in the field. We propose the use of a logistic regression classifier based on temporal aligned Electrocardiograms, and the utilisation of interp...