scholarly journals Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology

2021 ◽  
Vol 66 ◽  
pp. 102462
Author(s):  
Gergely Tuboly ◽  
György Kozmann ◽  
Orsolya Kiss ◽  
Béla Merkely
Keyword(s):  
Atrial Fibrillation ◽  
Activity Analysis ◽  
Atrial Activity ◽  
Atrial Fibrillation Detection

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

2021 ◽  
Vol 169 ◽  
pp. 114452
Author(s):  
Gerald Hirsch ◽  
Søren H. Jensen ◽  
Erik S. Poulsen ◽  
Sadasivan Puthusserypady
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Hybrid Approach ◽  
Atrial Activity ◽  
Atrial Fibrillation Detection

scholarly journals Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Complex Network ◽  
Individual Variation ◽  
Deep Neural Network ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Electrocardiogram Signal ◽  
Sensitivity Specificity ◽  
Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry

2018 ◽  
Vol 251 ◽  
pp. 45-50 ◽  
Author(s):  
Jorge Pagola ◽  
Jesus Juega ◽  
Jaume Francisco-Pascual ◽  
Angel Moya ◽  
Mireia Sanchis ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pilot Study ◽  
Acute Phase ◽  
Atrial Fibrillation Detection

Abstract 14839: High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Terrence Pong ◽  
Joy Aparicio Valenzuela ◽  
Kevin J Cyr ◽  
Cody Carlton ◽  
Sasank Sakhamuri ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Resolution ◽  
Structural Organization ◽  
Porcine Model ◽  
Dominant Frequency ◽  
Patient Specific ◽  
Structural Remodeling ◽  
Atrial Activity ◽  
Spatiotemporal Changes ◽  
Mapping Arrays

Introduction: Spatiotemporal differences in atrial activity are thought to contribute to the maintenance of atrial fibrillation (AF). While recent evidence has identified changes in dominant frequency (DF) during the transition from paroxysmal to persistent AF, little is known about the frequency characteristics of the epicardium during this transition. The purpose of this study was to perform high-resolution mapping of the atrial epicardium and to characterize changes in frequency activity and structural organization during the transition from paroxysmal to persistent AF. Hypothesis: In a porcine model of persistent AF, we tested the hypothesis that the epicardium undergoes spatiotemporal changes in atrial activity and structural organization during persistent AF. Methods: Paroxysmal and persistent AF was induced in adult Yorkshire swine by atrial tachypacing. Atrial morphology was segmented from magnetic resonance imaging and high-resolution patient-specific flexible mapping arrays were 3D printed to match the epicardial contours of the atria. Epicardial activation and DF mapping was performed in four paroxysmal and four persistent AF animals using personalized mapping arrays. Histological analysis was performed to determine structural differences between paroxysmal and persistent AF. Results: The left atrial epicardium was associated with a significant increase in DF between paroxysmal and persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). High-resolution spatiotemporal mapping identified organized clusters of DF during paroxysmal AF which were lost during persistent AF. The development of persistent AF led to structural remodeling with increased atrial epicardial fibrosis. The organization index (OI) significantly decreased during persistent AF in both the left atria (0.3 ± 0.03 vs. 0.2 ± 0.03, P = 0.01) and right atria (0.33 ± 0.04 vs. 0.23 ± 0.02, P = 0.02). Conclusions: In the porcine model of persistent AF, the epicardium undergoes structural remodeling with increased epicardial fibrosis, reflected by changes in atrial organization index and dominant frequency.

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