Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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 …
Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome
Shortening of atrial refractory period during atrial fibrillation has been considered a hallmark
of atrial electrical remodelling. The atrial fibrillatory cycle length, which is intimately related to …
of atrial electrical remodelling. The atrial fibrillatory cycle length, which is intimately related to …
Optimal parameters study for sample entropy-based atrial fibrillation organization analysis
Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection
of three parameters: the length of the sequences to be compared, m, the patterns similarity …
of three parameters: the length of the sequences to be compared, m, the patterns similarity …
[HTML][HTML] Using minimum redundancy maximum relevance algorithm to select minimal sets of heart rate variability parameters for atrial fibrillation detection
Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node.
In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete …
In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete …
An echo state neural network for QRST cancellation during atrial fibrillation
A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in
recordings with two or more leads. The method is based on an echo state neural network …
recordings with two or more leads. The method is based on an echo state neural network …
Atrial fibrillation burden estimation using multi-task deep convolutional neural network
Atrial fibrillation (AF) burden is defined as the percentage of time the patient is in AF rhythm
during a certain monitoring period. The accurate AF burden estimation from the long-term …
during a certain monitoring period. The accurate AF burden estimation from the long-term …
Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings
The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as
being either paroxysmal or persistent is addressed by investigating a robust approach to …
being either paroxysmal or persistent is addressed by investigating a robust approach to …
Long-term frequency gradients during persistent atrial fibrillation in sheep are associated with stable sources in the left atrium
D Filgueiras-Rama, NF Price, RP Martins… - Circulation …, 2012 - ahajournals.org
Background—Dominant frequencies (DFs) of activation are higher in the atria of patients
with persistent than paroxysmal atrial fibrillation (AF), and left atrial (LA)-to-right atrial (RA) …
with persistent than paroxysmal atrial fibrillation (AF), and left atrial (LA)-to-right atrial (RA) …
Atrial fibrillation detection with signal decomposition and dilated residual neural network
Y Li, Y **a - Physiological Measurement, 2023 - iopscience.iop.org
Objective. Detecting atrial fibrillation (AF) using electrocardiogram (ECG) recordings from
wearable devices has been challenging due to factors such as low signal-to-noise ratio and …
wearable devices has been challenging due to factors such as low signal-to-noise ratio and …
Noninvasive time and frequency predictors of long‐standing atrial fibrillation early recurrence after electrical cardioversion
Background: Several clinical factors have been studied to predict atrial fibrillation (AF)
recurrence after electrical cardioversion (ECV) with limited predictive value. Methods: A …
recurrence after electrical cardioversion (ECV) with limited predictive value. Methods: A …