scholarly journals Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation

2019 ◽  
Vol 104 ◽  
pp. 299-309 ◽  
Author(s):  
Frederique J. Vanheusden ◽  
Gavin S. Chu ◽  
Xin Li ◽  
João Salinet ◽  
Tiago P. Almeida ◽  
...  
2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Anita Ahmad

Atrial Fibrillation (AF) is the most common disorder of the heart rhythms. There are about 2.3 million people in United States and 4.5 million people in the European Union with AF [1]. It is also one of the factors that may contribute to mortality and morbidity. Researchers who apply spectral techniques show that certain areas of the atria can have higher activation frequencies than other areas. Frequency analysis is used to measure changes in Dominant Frequency (DF). We access the electrical propagation inside the atria by spectrogram plotting and examining the effect of high frequency stimulation on human.


Author(s):  
Victor Gonçalves Marques ◽  
Miguel Rodrigo ◽  
Maria de la Salud Guillem Sánchez ◽  
João Salinet

2021 ◽  
Vol 11 ◽  
Author(s):  
Miguel Rodrigo ◽  
Kian Waddell ◽  
Sarah Magee ◽  
Albert J. Rogers ◽  
Mahmood Alhusseini ◽  
...  

Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings.Methods: In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings.Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 – 1.37] across bi-atrial regions (R2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 – 11 ms) [0.03 – 0.42] for patient-level comparisons (R2 = 0.62), and 0.19 Hz [0.03 – 0.59] and 0.20 Hz [0.04 – 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success (p = 0.04).Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Jacobsen ◽  
T.A Dembek ◽  
A.P Ziakos ◽  
G Kobbe ◽  
M Kollmann ◽  
...  

Abstract Background Atrial fibrillation (A-fib) is the most common arrhythmia; however, detection of A-fib is a challenge due to irregular occurrence. Purpose Evaluating feasibility and performance of a non-invasive medical wearable for detection of A-fib. Methods In the CoMMoD-A-fib trial admitted patients with a high risk for A-fib carried the wearable and an ECG Holter (control) in parallel over a period of 24 hours under not physically restricted conditions. The wearable with a tight-fit upper arm band employs a photoplethysmography (PPG) technology enabling a high sampling rate. Different algorithms (including a deep neural network) were applied to 5 min PPG datasets for detection of A-fib. Proportion of monitoring time automatically interpretable by algorithms (= interpretable time) was analyzed for influencing factors. Results In 102 inpatients (age 71.0±11.9 years; 52% male) 2306 hours of parallel recording time could be obtained; 1781 hours (77.2%) of these were automatically interpretable by an algorithm analyzing PPG derived intervals. Detection of A-Fib was possible with a sensitivity of 92.7% and specificity of 92.4% (AUC 0.96). Also during physical activity, detection of A-fib was sufficiently possible (sensitivity 90.1% and specificity 91.2%). Usage of the deep neural network improved detection of A-fib further (sensitivity 95.4% and specificity 96.2%). A higher prevalence of heart failure with reduced ejection fraction was observed in patients with a low interpretable time (p=0.080). Conclusion Detection of A-fib by means of an upper arm non-invasive medical wearable with a high resolution is reliably possible under inpatient conditions. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Internal grant program (PhD and Dr. rer. nat. Program Biomedicine) of the Faculty of Health at Witten/Herdecke University, Germany. HELIOS Kliniken GmbH (Grant-ID 047476), Germany


2021 ◽  
Vol 77 (18) ◽  
pp. 280
Author(s):  
Miguel Rodrigo ◽  
Tina Baykaner ◽  
Wouter-Jan Rappel ◽  
Sanjiv Narayan

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