IEEE

Beat-by-Beat Classification of ECG Signals Using Machine Learning Algorithms to Detect PVC ...

Abstract: A premature ventricular contraction (PVC) disrupt the normal heart rhythm and indicate underlying cardiac disease. We aim to detect these PVC beats from electrocardiogram (ECG/EKG) data by ...
ahajournals.org

Abstract 4368538: Fusion Machine Learning Architectures for Improving ECG classification of ...

Introduction: Acute coronary syndrome (ACS) is a life-threatening emergency, with occlusion myocardial infarction (OMI) requiring rapid diagnosis and treatment. The 12-lead ECG remains the primary ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Rochester Institute of Technology

RIT researchers use machine learning to better understand the pathways of disease

Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...
ahajournals.org

Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A ...

Faculty of Medicine & Hospital das Clínicas/EBSERH, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (L.C.C.B., S.M.B., A.L.P.R.). Telehealth Center ...
IEEE

Machine Learning-Based Stroke Prediction and Heartbeat Classification Using ECG and PPG ...

Abstract: Timely stroke detection is essential for effective treatment and improved patient outcomes. Traditional methods like MRI and CT scans, while informative, are often expensive and ...
GitHub

Sleep Apnea Classification using Deep Learning on ECG Signals

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

An Evaluation of Machine Learning Models for Threat Classification in IoT Devices ()

1 Center for Cyberspace Studies, Nasarawa State University, Keffi, Nigeria. 2 Department of Computer Engineering, Nile University of Nigeria, Abuja, Nigeria. 3 Department of Public and International ...