Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...
UC Berkeley researchers say a new AI model found a hidden ECG signal that could help doctors identify more people at risk ...
Patent covers machine learning techniques for ECG denoising, rhythm classification, sample-level labelling, wearable cardiac ...
Abstract: Sleep stage classification is essential for diagnosing sleep disorders. However, the clinical reliance on polysomnography (PSG) faces significant challenges due to its high cost, ...
Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally. Effective management hinges on early diagnosis, which is often impeded by non-specific symptoms and ...
Authors: Monica Kraft, MD, Health System Chair of the Department of Medicine; Girish N. Nadkarni, MD, MPH, CPH, Chief AI Officer and Chair of the Department of Artificial Intelligence and Human Health ...
This repository includes the code of the ECG-DualNet for ECG classification proposed in the paper Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in ...
We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, ...
Abstract: The 12-lead electrocardiogram (ECG) method can diagnose more cardiovascular disease than the single-lead method, but it is difficult to use in daily life because numerous electrodes must be ...
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