Atrial fibrillation

Miyazawa, Kazuo; Lip, Gregory Y.H.

doi:10.1016/j.mpmed.2018.07.009

Cited by 8 publications

(11 citation statements)

References 5 publications

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“…In many patients the arrhythmia exhibits a progressive nature, being commonly classified into four major groups according to its course, i.e., paroxysmal, persistent, long-standing persistent, and permanent AF [ 8 ]. Paroxysmal AF is the first manifestation of the disease and shows self-terminating episodes lasting for less than seven days.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Cirugeda-Roldán

Calero

Manuel

et al. 2020

Entropy

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Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20–30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3–9% and other previously proposed predictors of ECV by 15–30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

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Section: Introductionmentioning

confidence: 99%

“…When the arrhythmia lasts for more than a year, it is called long-standing persistent AF. Finally, when it is impossible to restore sinus rhythm (SR), both the patient and the clinician make a joint decision to avoid more interventions for that purpose and AF is labelled as permanent [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Cirugeda-Roldán

Calero

Manuel

et al. 2020

Entropy

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“…Among cardiac arrhythmias, atrial fibrillation is of particular concern because of its recognized increased risk of stroke, heart failure, morbidity and all-cause of mortality [1]. Referred to as a "silent killer" because it does not always produce symptoms that alert patients/Doctors to the problem, its automatic detection is strongly advisable.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Haddi¹,

Ananou²,

Trardi³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity.

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“…5 Importantly, AF independently increases all-cause mortality and morbidity such as stroke, systemic embolism, and heart failure and prolongs the hospital length of stay. [4][5][6] Mechanisms of new-onset AF in critically ill patients in the ICU and in cardiac surgery patients are only partly elucidated, but the 2 populations share several AF triggers. 4,5 Overall, new-onset AF is considered to be benign.…”

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confidence: 99%

“…Stroke prevention is the cornerstone of management of patients with AF in the general population. 6 Although the long-term risk of thromboembolism seems to be lower in cardiac surgery patients compared with patients with nonsurgical, nonvalvular AF, 8 we have to learn from the knowledge gained in other patient cohorts. Of interest is the integrated care management of AF, the Atrial Fibrillation Better Care (ABC) pathway-"A" (avoid stroke), "B" (better symptom management), and "C" (cardiovascular and comorbidity risk management).…”

mentioning

confidence: 99%