Issues in the automated classification of multilead ECGs using heterogeneous labels and populations

MA Reyna, N Sadr, EAP Alday, A Gu… - Physiological …, 2022‏ - iopscience.iop.org
Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …

[HTML][HTML] Atrioventricular synchronization for detection of atrial fibrillation and flutter in one to twelve ECG leads using a dense neural network classifier

I Jekova, I Christov, V Krasteva - Sensors, 2022‏ - mdpi.com
This study investigates the use of atrioventricular (AV) synchronization as an important
diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart …

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification

B Puszkarski, K Hryniów, G Sarwas - Physiological Measurement, 2022‏ - iopscience.iop.org
Objective. The primary purpose of this work is to analyze the ability of N-BEATS architecture
for the problem of prediction and classification of electrocardiogram (ECG) signals. To …

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation

PA Warrick, V Lostanlen, M Eickenberg… - Physiological …, 2022‏ - iopscience.iop.org
We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead
electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase …

Impact of Pre-Processing Decisions on Automated ECG Classification Accuracy

AK Cornely, GM Mirsky - 2022 Computing in Cardiology (CinC), 2022‏ - ieeexplore.ieee.org
Electrocardiography is well established as an effective clinical tool for detection and
diagnosis of cardiac arrhythmias and abnormalities. The objective of the 2021 …

[فهرست منابع][C] Alvis–a scientific review

V Rehnberg - 2022