IEEE

Certain Examination on ECG Classification using Hybrid Machine Learning Models

Abstract: A Non-stationary signal called an electrocardiogram (ECG) is utilized to evaluate the rate of cardiac beats. Doctors can identify Abstract - Heart Rate Variations (HRV) is one of the major ...
C&EN

Developing a Community-Specific Daily Weather Health Risk Index Across Australia Using Explainable Machine Learning

Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia ...
ahajournals.org

Abstract 4368538: Fusion Machine Learning Architectures for Improving ECG classification of acute coronary events

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 ...
ahajournals.org

Abstract 4366733: Deep Learning-Based BNP Classification from Single-Lead ECG

Background: Brain natriuretic peptide (BNP) is a key heart failure biomarker. Single-lead electrocardiograms (ECGs) from wearable devices offer valuable diagnostic and prognostic insights. We ...
IEEE

Development and Validation of Feature-Based Machine Learning for ECG Artifact Detection and Classification

ECG signals are vital for diagnosing cardiovascular diseases, but artifacts like power line interference, baseline wander, and motion artifacts hinder accurate interpretation. This study aims to ...