Survey on atrial fibrillation detection from a single-lead ECG wave for Internet of Medical Things
Y Liu, J Chen, N Bao, BB Gupta, Z Lv - Computer Communications, 2021 - Elsevier
Recent advances of Internet of Medical Things have allowed for continuous heart rhythm
monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected …
monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning
M Rizwan, BM Whitaker… - Physiological …, 2018 - iopscience.iop.org
Objective: The objective of this paper is to provide an algorithm for accurate, automated
detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are …
detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are …
An improved electrocardiogram arrhythmia classification performance with feature optimization
A Darmawahyuni, S Nurmaini, B Tutuko… - BMC Medical Informatics …, 2024 - Springer
Background Automatic classification of arrhythmias based on electrocardiography (ECG)
data faces several significant challenges, particularly due to the substantial volume of …
data faces several significant challenges, particularly due to the substantial volume of …
A deep learning approach for detecting atrial fibrillation using RR intervals of ECG
Purpose: Atrial Fibrillation (AF) is one of the most common types of heart arrhythmias
observed in clinical practice. AF can be detected using an Electrocardiogram (ECG). ECG …
observed in clinical practice. AF can be detected using an Electrocardiogram (ECG). ECG …
Artificial intelligence and machine learning in cardiac electrophysiology
Cardiac electrophysiology requires the processing of several patient-specific data points in
real time to provide an accurate diagnosis and determine an optimal therapy. Expanding …
real time to provide an accurate diagnosis and determine an optimal therapy. Expanding …
An efficient method to learn overcomplete multi-scale dictionaries of ECG signals
The electrocardiogram (ECG) was the first biomedical signal for which digital signal
processing techniques were extensively applied. By its own nature, the ECG is typically a …
processing techniques were extensively applied. By its own nature, the ECG is typically a …
A signal processing framework for the detection of abnormal cardiac episodes
Motivation Cardiologists rely on the long duration Holter electrocardiogram (ECG)
recordings in general for assessment of abnormal episodes and such process found to be …
recordings in general for assessment of abnormal episodes and such process found to be …
Electrocardiographic fragmented activity (ii): A machine learning approach to detection
FM Melgarejo-Meseguer, FJ Gimeno-Blanes… - Applied Sciences, 2019 - mdpi.com
Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common
disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This …
disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This …
Electrocardiographic fragmented activity (I): physiological meaning of multivariate signal decompositions
FM Melgarejo-Meseguer, FJ Gimeno-Blanes… - Applied Sciences, 2019 - mdpi.com
Recent research has proven the existence of statistical relation among fragmented QRS and
several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome …
several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome …