Atrial Fibrillation Detection and Atrial Fibrillation Burden Estimation via Wearables

Author(s):  
Li Zhu ◽  
Viswam Nathan ◽  
Jilong Kuang ◽  
Jacob Kim ◽  
Robert Avram ◽  
...  
2018 ◽  
Vol 13 (9) ◽  
pp. 893-904 ◽  
Author(s):  
Tommaso Sanna

An increasing number of detection tools are available and several detection strategies have been described to pursue the diagnosis of atrial fibrillation to prevent ischemic stroke. Monitoring tools include standard electrocardiography, snapshot single-lead recordings with professional or personal devices (e.g. smartphone-based), Holter monitor, external devices with long-term recording capabilities, and cardiac implantable electronic devices, including pacemakers, implantable cardioverter defibrillators and insertable cardiac monitors. Insertable cardiac monitors have shown high sensitivity and specificity for the detection of atrial fibrillation, allow up to three years of continuous monitoring, do not require cooperation of the patient, are well tolerated, have a short device-related time delay between detection of atrial fibrillation and notification to the physician, provide information on atrial fibrillation burden and are minimally invasive. On the other hand, insertable cardiac monitors require a considerable use of resources to process the recordings and have a significant initial cost. Studies conducted with insertable cardiac monitors on patients with prior stroke and on patients with risk factors for stroke but no prior cerebrovascular events or atrial fibrillation have consistently shown a measurable incidence of atrial fibrillation at follow-up. However, the effectiveness of oral anticoagulations in reducing the incidence of ischemic stroke in patients with atrial fibrillation lasting less than 24 h, though reasonable, is currently unproven. The future of atrial fibrillation detection tools and atrial fibrillation detection strategies will be influenced by ongoing studies exploring whether oral anticoagulations reduce the incidence of stroke in patients with atrial fibrillation burden lower than 24 h.


2021 ◽  
Vol 34 ◽  
pp. 100791
Author(s):  
Victoria Jansson ◽  
Lennart Bergfeldt ◽  
Jonas Schwieler ◽  
Göran Kennebäck ◽  
Aigars Rubulis ◽  
...  

2020 ◽  
Vol 29 ◽  
pp. S174
Author(s):  
D. Makarious ◽  
A. Bhat ◽  
S. Khanna ◽  
H. Chen ◽  
A. Drescher ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  

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.


2018 ◽  
Vol 251 ◽  
pp. 45-50 ◽  
Author(s):  
Jorge Pagola ◽  
Jesus Juega ◽  
Jaume Francisco-Pascual ◽  
Angel Moya ◽  
Mireia Sanchis ◽  
...  

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