scholarly journals A Novel Method for Real-Time Atrial Fibrillation Detection in Electrocardiograms Using Multiple Parameters

2013 ◽  
Vol 19 (3) ◽  
pp. 217-225 ◽  
Author(s):  
Xiaochuan Du ◽  
Nini Rao ◽  
Mengyao Qian ◽  
Dingyu Liu ◽  
Jie Li ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Real Time ◽  
Multiple Parameters ◽  
Novel Method ◽  
Atrial Fibrillation Detection

A 290 mV Sub- $V_{\rm T}$ ASIC for Real-Time Atrial Fibrillation Detection

2015 ◽  
Vol 9 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Oskar Andersson ◽  
Ki H. Chon ◽  
Leif Sornmo ◽  
Joachim Neves Rodrigues
Keyword(s):  
Atrial Fibrillation ◽  
Real Time ◽  
Atrial Fibrillation Detection

Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device

2020 ◽  
Vol 116 ◽  
pp. 103540 ◽  
Author(s):  
Italo Agustin Marsili ◽  
Luca Biasiolli ◽  
Michela Masè ◽  
Alberto Adami ◽  
Alberto Oliver Andrighetti ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Real Time ◽  
Atrial Fibrillation Detection

Improvements in atrial fibrillation detection for real-time monitoring

2009 ◽  
Vol 42 (6) ◽  
pp. 522-526 ◽  
Author(s):  
Saeed Babaeizadeh ◽  
Richard E. Gregg ◽  
Eric D. Helfenbein ◽  
James M. Lindauer ◽  
Sophia H. Zhou
Keyword(s):  
Atrial Fibrillation ◽  
Real Time ◽  
Real Time Monitoring ◽  
Atrial Fibrillation Detection

scholarly journals A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0136544 ◽  
Author(s):  
Xiaolin Zhou ◽  
Hongxia Ding ◽  
Wanqing Wu ◽  
Yuanting Zhang
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Real Time ◽  
Detection Algorithm ◽  
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.

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