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

scholarly journals Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry)

10.2196/11437 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e11437 ◽  
Author(s):  
Yong-Yan Fan ◽  
Yan-Guang Li ◽  
Jian Li ◽  
Wen-Kun Cheng ◽  
Zhao-Liang Shan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pilot Study ◽  
Diagnostic Performance ◽  
Smart Device ◽  
Atrial Fibrillation Detection

Changes of QT interval in the acute phase after pulmonary vein isolation for paroxysmal atrial fibrillation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Chikata ◽  
T Kato ◽  
K Ududa ◽  
S Fujita ◽  
K Otowa ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulmonary Vein ◽  
Female Patient ◽  
Pulmonary Vein Isolation ◽  
Acute Phase ◽  
Paroxysmal Atrial Fibrillation ◽  
Qt Interval ◽  
Qt Prolongation ◽  
Qtc Interval ◽  
The Impact

Abstract Introduction Pulmonary vein isolation (PVI) affects ganglionated plexi (GP) around the atrium, leading to a modification of the intrinsic cardiac autonomic system (ANS). In animal models, GP ablation has a potential risk of QT prolongation and ventricular arrhythmias. However, the impact of PVI on QT intervals in humans remains unclear. Purpose This study aims to evaluate the Impact of PVI on QT interval in patients with paroxysmal atrial fibrillation. Methods We analyzed consecutive 117 PAF patients for their first PVI procedures. 12-lead ECG was evaluated at baseline, 4 hr, day 1, 1 month, and 3 months after ablation. Only patients with sinus rhythm on 12-lead ECG at each evaluation point without antiarrhythmic drugs were included. Results Heart rate significantly increased at 4 hr, day 1, and 1 month. Raw QT interval prolonged at 4 hr (417.1±41.6 ms, P<0.001) but shortened at day 1 (376.4±34.1 ms, P<0.001), 1 month (382.2±31.5 ms, P<0.001), and 3 months (385.1±32.8 ms, P<0.001) compared to baseline (391.6±31.4 ms). Bazett- and Fridericia- corrected QTc intervals significantly prolonged at 4hr (Bazett: 430.8±27.9 ms, P<0.001; Fridericia: 425.8±27.4 ms, P<0.001), day1 (Bazett: 434.8±22.3 ms, P<0.001; Fridericia: 414.1±23.7 ms, P<0.001), 1M (Bazett: 434.8±22.3 ms, P<0.001; Fridericia: 408.2±21.0 ms, P<0.05), and 3M (Bazett: 420.1±21.8 ms, P<0.001; Fridericia: 407.8±21.1 ms, P<0.05) compared to baseline (Bazett: 404.9±25.2 ms; Fridericia: 400.0±22.6 ms). On the other hand, Framingham- and Hodges- corrected QTc interval significantly prolonged only at 4hr (Framingham: 424.1±26.6 ms, P<0.001; Hodges: 426.8±28.4 ms, P<0.001) and at day1 (Framingham: 412.3±29.3 ms, P<0.01; Hodges: 410.6±40.2 ms, P<0.05) compared to baseline (Framingham: 399.2±22.7 ms; Hodges: 400.7±22.8 ms). At 4 hr after ablation, raw QT and QTc of all formulas significantly prolonged than baseline. Raw QT and QTc prolongation at 4hr after ablation were more frequently observed in female patients. Multiple regression analysis revealed that female patient is a significant predictor of raw QT and QTc interval prolongation of all formulas 4hr after PVI. Conclusions Raw QT and QTc prolonged after PVI, especially in the acute phase. Female patient is a risk factor for QT prolongation in the acute phase after PVI. Funding Acknowledgement Type of funding source: None

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.

scholarly journals Investigation of the solubility and the potentials for purification of serum amyloid A (SAA) from equine acute phase serum – a pilot study

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Michelle B Christensen ◽  
Jens Christian Sørensen ◽  
Stine Jacobsen ◽  
Mads Kjelgaard-Hansen
Keyword(s):  
Pilot Study ◽  
Acute Phase ◽  
Serum Amyloid A ◽  
Serum Amyloid ◽  
Amyloid A ◽  
Acute Phase Serum
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