Background Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability.
Meghan Hegarty-Craver is a biomedical research engineer specializing in signal processing for wearable sensors, extracting heart rate and heart rate variability metrics from electrocardiogram (ECG) ...
This project aims to develop an affordable, battery-powered, machine learning-based portable device to detect Atrial Fibrillation (AFib) using Photoplethysmography (PPG). AFib, a common cardiac ...
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking ...
Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these ...
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Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful ...
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