Researchers at Penn State are using machine learning and existing electrocardiogram (ECG) data to help doctors make more ...

In this study, we introduce a deep learning model designed to assist cardiologists in the early diagnosis of PAF during NSR by classifying ECGs into Healthy-NSR ECG and PAF-NSR ECG categories. We ...

Researchers at Penn State are using machine learning and existing electrocardiogram (ECG) data to help doctors make more ...

Introduction: Although left bundle branch area pacing (LBBAP) has become a popular approach to physiologic pacing in heart failure (HF), there is still substantial uncertainty regarding ECG predictors ...

Introduction: While smartwatches that acquire Lead-I electrocardiogram (ECG) have shown promise in detecting arrhythmias, the detection of ischemic events using Lead-I alone remains understudied. We ...

Worldwide, over 300 million electrocardiograms (EKGs) are performed each year, with one-third of those taking place in the ...

Listen to Story Lab attendant in Jodhpur performs ECG after learning from YouTube Patient's family requested for qualified staff, but were ignored Attendant claimed no staff available due to Diwali, ...

Recent advances in computer processing capabilities and the advent of next-generation predictive machine learning (ML ... studies have attempted to utilize ECG-data-trained artificial intelligence ...

Ambulatory electrocardiographic (ECG) monitoring is used to help medical professionals diagnose intermittent cardiac arrhythmias that occur only infrequently and unpredictably. Such arrhythmias often ...

as well as the introduction of next-generation predictive machine learning (ML) models, have generated interest in the research community. Since 2020, a few studies have attempted to use ECG-data ...