scholarly journals Low Energy ECG Features Extraction for Atrial Fibrillation Detection in Wearable Sensors

2021 ◽  
Author(s):  
Manan AlMusallam ◽  
Adel Soudani
Keyword(s):  
Atrial Fibrillation ◽  
Wearable Sensors ◽  
Features Extraction ◽  
Low Energy ◽  
Atrial Fibrillation Detection

Prospective randomized trial of transthoracic versus low-energy transvenous internal cardioversion in persistent atrial fibrillation

2004 ◽  
Vol 59 (5) ◽  
pp. 521-526 ◽  
Author(s):  
Rıdvan YALÇIN ◽  
Mehmet Güngör KAYA ◽  
Murat OZDEMIR ◽  
Mustafa CEMRI ◽  
Timur TIMURKAYNAK ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Randomized Trial ◽  
Persistent Atrial Fibrillation ◽  
Low Energy ◽  
Prospective Randomized Trial ◽  
Internal Cardioversion

scholarly journals Low energy internal cardioversion of atrial fibrillation resistant to transthoracic shocks.

Heart ◽  
1996 ◽  
Vol 75 (6) ◽  
pp. 635-638 ◽  
Author(s):  
S. M. Sopher ◽  
F. D. Murgatroyd ◽  
A. K. Slade ◽  
I. Blankoff ◽  
E. Rowland ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Low Energy ◽  
Internal Cardioversion

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
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