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New Studies: AI Captures Electrocardiogram Patterns That Could Signal a Future Sudden Cardiac Arrest

DAIC

In a study published in Communications Medicine , David Ouyang, MD, assistant professor of Cardiology and Medicine at Cedars-Sinai, along with Chugh and fellow investigators trained a deep learning algorithm to study patterns in electrocardiograms, also known as ECGs, which are recordings of the heart’s electrical activity.

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Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms

HeartRhythm

Artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS).

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AI-based preeclampsia detection and prediction with electrocardiogram data

Frontiers in Cardiovascular Medicine

In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby.

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Researchers achieve contactless electrocardiogram monitoring

Medical Xpress - ECG

Chen Yan and researcher Sun Qibin from the University of Science and Technology of China (USTC) achieved contactless electrocardiogram (ECG) monitoring through a millimeter-wave radar system. Recently, a team led by Prof. Their work was published in IEEE Transactions on Mobile Computing and reported by IEEE Spectrum.

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New electrode design promises major improvements in wearable electrocardiograms

Medical Xpress - ECG

That's why the recent and rapid rise in wearable electronic health-monitoring devices with heart rate-measuring electrocardiograms (ECG) represents a significant step forward. Nearly 200 million people around the globe have coronary heart disease, which accounts for about one in every six deaths, according to the British Heart Foundation.

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Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

Frontiers in Cardiovascular Medicine

Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician’s expertise. Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates.

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Researchers use new deep learning approach to enable analysis of electrocardiograms as language

Medical Xpress - ECG

Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that allows for the interpretation of ECGs as language.