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A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms
The application of non-linear metrics to physiological signals is a valuable tool because
“hidden information” related to underlying mechanisms can be obtained. In this respect …
“hidden information” related to underlying mechanisms can be obtained. In this respect …
Prediction of atrial fibrillation using machine learning: a review
AS Tseng, PA Noseworthy - Frontiers in Physiology, 2021 - frontiersin.org
There has been recent immense interest in the use of machine learning techniques in the
prediction and screening of atrial fibrillation, a common rhythm disorder present with …
prediction and screening of atrial fibrillation, a common rhythm disorder present with …
Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems
Cardiovascular diseases are the number one cause of death worldwide. Currently, portable
battery-operated systems such as mobile phones with wireless ECG sensors have the …
battery-operated systems such as mobile phones with wireless ECG sensors have the …
Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis
This paper presents a novel approach for QRS complex detection and extraction of
electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered …
electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered …
Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal
Abstract Background and Objective Paroxysmal Atrial Fibrillation (PAF) is one of the most
common major cardiac arrhythmia. Unless treated timely, PAF might transform into …
common major cardiac arrhythmia. Unless treated timely, PAF might transform into …
[HTML][HTML] Prediction of paroxysmal atrial fibrillation using new heart rate variability features
Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart
failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any …
failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any …
Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal
In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is
presented, which is based on analysis of the heart rate variability (HRV) signal. The …
presented, which is based on analysis of the heart rate variability (HRV) signal. The …
Electrocardiographic predictors of atrial fibrillation
BACKGROUND: Atrial fibrillation (AF) is the most prevalent arrhythmia in the United States
and accounts for more than 750,000 strokes per year. Noninvasive predictors of AF may …
and accounts for more than 750,000 strokes per year. Noninvasive predictors of AF may …
A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors
This paper presents a robust QRS detection algorithm that is capable of detecting QRS
complexes as well as accurately identifying R-peaks. The proposed bilateral threshold …
complexes as well as accurately identifying R-peaks. The proposed bilateral threshold …
[PDF][PDF] Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal
U Erdenebayar, H Kim, JU Park… - Journal of Korean …, 2019 - synapse.koreamed.org
Background: In this study, we propose a method for automatically predicting atrial fibrillation
(AF) based on convolutional neural network (CNN) using a short-term normal …
(AF) based on convolutional neural network (CNN) using a short-term normal …