Rakeness-Based Compressed Sensing of Atrial Electrograms for the Diagnosis of Atrial Fibrillation

2019 ◽  
Author(s):  
Samprajani Rout ◽  
Mauro Mangia ◽  
Fabio Pareschi ◽  
Gianluca Setti ◽  
Riccardo Rovatti ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Atrial Electrograms

scholarly journals Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Eva María Cirugeda-Roldán ◽  
Antonio Molina Picó ◽  
Daniel Novák ◽  
David Cuesta-Frau ◽  
Vaclav Kremen
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rhythm ◽  
Heart Tissue ◽  
Sample Entropy ◽  
Mapping Technique ◽  
Atrial Electrograms ◽  
Invasive Method ◽  
Atrial Electrogram ◽  
Focal Atrial Tachycardia ◽  
Class Segmentation

Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.

Abstract 5659: Regular Atrial Tachycardias After Ablation Of Complex Fractionated Atrial Electrograms (CFAE) For Atrial Fibrillation: Acute Incidence And Recurrence In Clinical Follow-up

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Tilko Reents ◽  
Gabriele Hessling ◽  
Stephanie Fichtner ◽  
Jinjin Wu ◽  
Heidi L Estner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Successful Ablation ◽  
Atrial Electrograms ◽  
Initial Procedure ◽  
The Impact ◽  
External Cardioversion ◽  
Complex Fractionated Atrial Electrograms

Background: The catheter ablation of atrial fibrillation (AF) can be performed by ablation of complex fractionated atrial electrograms (CFAE). Endpoint of CFAE ablation is the regularisation or termination of AF. However, the impact of regular atrial tachycardia (AT) occurring during CFAE ablation on long term outcome has not been investigated. Thus, it is not clear whether these tachycardias should be acutely targeted for ablation. Methods: In 43 patients (31 male, age 62±9 years with paroxysmal (15 patients), persistent (25 patietns) or permanent AF (3 patients) organisation of AF to regular AT was achieved by ablation of CFAE. Mapping of AT with subsequent successful ablation was performed in 14/43 patients (33%), in the remaining 29/43 patients (67%) AT was terminated with external cardioversion or pace overdrive. After ablation procedure, patients were seen in our out-patient clinic with repetitive Holter ECG after 1, 3, and subsequently every 3 months and were intensively screened for the occurrence of regular AT. Results: In follow-up 22/43 patients (51%) developed sustained AT necessitating in 20 patients repeat catheter ablation (12 patients) or external cardioversion (8 patients). AF had been paroxysmal in 7/22 and persisten in 15/22 patients with AT in follow-up. In 14/22 patients (63%), no attempt for ablation of AT had been made during the initial procedure, in 8/22 AT (36%) had been mapped and initially successful ablated. Of 21 patients without AT occurrence during follow-up, AF had been paroxysmal in 8/21 and persistent or permanent in 13/21 patients. AT had been mapped and ablated in 6 (29%) whereas in 15/21 patients (71%), AT had not been targeted. Ablation of AT during initial procedure, number of ablation applications, procedure and fluoroscopy duration were not predictive for freedom of AT in follow-up. Conclusion: In our study, mapping and successful ablation of new onset regular atrial tachycardias (AT) occurring during ablation of CFAE for atrial fibrillation was not predictive for the occurrence of AT in follow-up. Thus, results after termination of AT by cardioversion was in long-term comparable to sometimes time-consuming mapping/ablation for AT.

Pitfalls in the definition of complex fractionated atrial electrograms for atrial fibrillation studies

2019 ◽  
Vol 31 (1) ◽  
pp. 373-374
Author(s):  
Tiago P. Almeida ◽  
Xin Li ◽  
Diogo C. Soriano ◽  
Fernando S. Schlindwein ◽  
G. André Ng
Keyword(s):  
Atrial Fibrillation ◽  
Atrial Electrograms ◽  
Definition Of ◽  
Complex Fractionated Atrial Electrograms

scholarly journals Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 436
Author(s):  
Yunfei Cheng ◽  
Ying Hu ◽  
Mengshu Hou ◽  
Tongjie Pan ◽  
Wenwen He ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Prior Information ◽  
Classification Performance ◽  
Experimental Results ◽  
Measurement Matrix ◽  
Ecg Signals ◽  
Proposed Model ◽  
Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

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