IEEE

Efficient ECG Anomaly Detection Using Autoencoder-Based Dimensionality Reduction: A Comparative Study of Machine Learning Algorithms

Abstract: Electrocardiograms (ECGs) are widely used to diagnose various cardiovascular conditions. While deep learning models have shown high accuracy in ECG classification, challenges such as high ...
News-Medical.Net

Machine learning model can predict 28-day mortality in sepsis patients

Sepsis is one of the most common and lethal syndromes encountered in intensive care units (ICUs), and acute respiratory ...
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 ...
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 ...
EDN

DIY EKG Using Arduino and Javascript

An electrocardiogram or EKG is a testing equipment used to measure the electrical activity of the heart. Using only an Arduino Shield and a set of electrodes, one can create his own EKG, generate a ...