Abstract: Convolutional Neural Networks (CNNs) have become a dominant solution for Electrocardiogram (ECG) beat classification, owing to their superior feature extraction capabilities. However, ...

Use of an ECG patch to remotely screen older patients at moderate-to-high stroke risk demonstrated modest benefits in long-term atrial fibrillation (AFib) diagnosis and anticoagulation exposure, based ...

Cannabis use showed no significant association with ECG abnormalities in people with HIV infection, although women had higher odds of abnormal ECG findings than men. Men with HIV infection had higher ...

A new artificial intelligence model found previously undetected signals in routine heart tests that strongly predict which patients will suffer potentially deadly complications after surgery. The ...

Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Background: Sleep apnea is a common sleep disorder associated with high ...

Introduction: The increasing prevalence of type 2 diabetes mellitus (T2DM) requires improved early detection strategies that integrate demographic, clinical, physiological, and pharmacological data.

Dr. McBain studies policies and technologies that serve vulnerable populations. On any given night, countless teenagers confide in artificial intelligence chatbots — sharing their loneliness, anxiety ...

The model is trained solely on ECG signals using HRV and EDR features with an LSTM-based neural network.

Abstract: Accurate classification of cardiac activity using electrocardiogram (ECG) signals is crucial in preventing and treating many heart-related diseases. However, the complex and non-stationary ...