Beat to beat wavelet variability in atrial fibrillation

Filos, Dimitrios; Chouvarda, Ioanna; Dakos, G.; Vassilikos, Vassilios; Maglaveras, Nicos

doi:10.1109/iembs.2011.6090215

Cited by 4 publications

(1 citation statement)

References 10 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last 20 years, several efforts have been made to predict AF by analysing P-wave morphology and other ECG features, but no results have been strong enough to be deemed usable in clinical practice. 20 Atrial fibrillation activity causes myocyte changes that lead to inflammation resulting in atrial fibrosis. 21 The tachycardia caused by AF leads to decreased contractility in the atria that eventually leads to atrial dilatation.…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening

et al. 2023

View full text Add to dashboard Cite

Aims Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. Methods and results A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence–based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78–0.83], which had a wide age range of 65–90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75–76 years), with AUCs of 0.62 (CI 0.61–0.64) and 0.62 (CI 0.58–0.65), respectively. Conclusion An artificial intelligence–enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.

show abstract

Section: Discussionmentioning

confidence: 99%

An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening

et al. 2023

View full text Add to dashboard Cite

show abstract

Wavelet variability of SA node originated P waves in atrial fibrillation and in signals with ectopic beats

Filos

Chouvarda

Dakos

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Atrial fibrillation (AF) is one of the most common cardiac arrhythmia. Electrical properties of the atrial myocardium may be related to the appearance of this type of arrhythmia. However ectopic beats, occurring normally in healthy people, disturb cardiac rhythm. Those beats arise from fiber outside the region of SA node. With this work we aim at highlighting differences in the atrial activity between healthy subjects, healthy subjects presenting many ectopic events and patients with AF. The X-Y-Z leads of vectorcardiography recordings are considered. Wavelet-based parameters are extracted from a window which represents atrial activity originated from SA node and compared between signals of the three groups. Results show differences potentially related to the conduction system of the atrium between healthy people and people with AF, as well as between healthy people and people with ectopic events. No difference was found from the analysis of SA node beats between people with AF and healthy with ectopic events.

show abstract

Two dimensional wavelet energy analysis on a beat to beat basis: Application to Atrial Fibrillation

Filos

Chouvarda

Dakos

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Atrial Fibrillation (AF) is a condition in which heart rhythm is not associated with normal sinoatrial (SA) node pacemaker but it derives from different areas on the atrium, often from the area of Pulmonary veins (PVs) A way to eliminate the influence of PVs in the inducement of AF is the PVs isolation surgery. In this study, an effort is made towards investigating the morphology and dynamics of P-waves, when the potentially arrhythmogenic tissue in PVs is involved or isolated via ablation. For this reason, 20 patients who were subjected to PVs isolation were studied, via vectrorcardiography recordings obtained before and after the ablation. Wavelet energies for five frequency bands were analyzed, using a two dimensional representation. The proposed technique was applied for the analysis of wavelet energies in consecutive beats, and their correlation with the RR interval. Features for the evaluation of those plots were extracted, such as the axes of a fitted to the plot ellipse and the center of the mass. The statistical analysis demonstrated significant differences between the groups, which imply the modification of the atrial substrate concerning electrical conduction toward to a more stable condition.

show abstract

Beat to beat wavelet variability in atrial fibrillation

Cited by 4 publications

References 10 publications

An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening

An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening

Wavelet variability of SA node originated P waves in atrial fibrillation and in signals with ectopic beats

Two dimensional wavelet energy analysis on a beat to beat basis: Application to Atrial Fibrillation

Contact Info

Product

Resources

About