Early Detection and Treatment of Atrial Arrhythmias Alleviates the Arrhythmic Burden in Paced Patients: The SETAM Study

Amara, W.; Montagnier, Christian; Cheggour, S.; Boursier, Michel; Gully, C.; Barnay, C; Georger, F.; Deplagne, Antoine; Fromentin, S.; Młotek, Marcin; Lazarus, Arnaud; Taïeb, J.; Investigators, Setam

doi:10.1111/pace.13062

Cited by 41 publications

(28 citation statements)

References 39 publications

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“…Clearly, the longer the delay, the more belated the identification of AF, thus reducing its clinical usefulness. Additionally, when most of brief AF episodes are undetected, only an imprecise AF burden can be computed, which could have a negative impact on further management of the patient [15]. In view of the obtained outcomes, this is not the case for the proposed combination of TQEn and COSEn.…”

Section: Discussionmentioning

confidence: 86%

“…Consequently, in addition to noise reduction, the averaging of consecutive TQ intervals also allowed the emphasis of the stable or variable nature of P-and -waves, respectively [26,27]. In order to quantify the effect of this averaging on the variability showed by the morphology of P-and -waves, values for of 2,5,10,15,20,30,40, and 50 beats were considered.…”

Section: Morphological Characterization Of P-and -Wavesmentioning

confidence: 99%

“…Currently, it is also clinically accepted that AF causes electrophysiological 2 Complexity alterations in the atrial tissue to favor its maintenance [9]. Hence, the detection of AF signs as early as possible is essential to enable successful preventive therapies and, thus, to reduce its burden [15]. However, about 90% of initial arrhythmic episodes have revealed to be asymptomatic [16] and, consequently, routine physical examination can only provide late diagnostic evidences [3].…”

Section: Introductionmentioning

confidence: 99%

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Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

et al. 2017

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Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram (ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.

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Section: Discussionmentioning

confidence: 86%

Section: Morphological Characterization Of P-and -Wavesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

et al. 2017

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“…Indeed, the management of AF nowadays involves a significant part of the resources from any Health Service in the developed countries [3]. Therefore, reducing the prevalence of this arrhythmia is a current priority [4], being its early detection a key aspect to avoid its natural progression and final perpetuation [5].…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Screening through Combined Timing Features of Short Single-Lead Electrocardiograms

García

Rodenas

Alcaraz

et al. 2017

Computing in Cardiology Conference (CinC)

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Atrial fibrillation (AF) is the most common cardiac arrhythmia, as well as a growing healthcare burden worldwide. It is often asymptomatic and usually starts with very brief episodes, thus making its early detection an interesting challenge. For that purpose, the present work introduces a novel method exploiting the variability presented both by ventricular and atrial activities reflected on the surface electrocardiogram (ECG). Thus, time series from the RR intervals and the fibrillatory waves morphology contained by the TQ intervals are first generated and, then, their regularity is estimated making use of the Coefficient of Sample Entropy (COSEn). The collected information is finally combined through a multi-class support vector machine (SVM) approach to discern among short episodes of AF, normal sinus rhythm (NSR) and other rhythms (OR). The algorithm has been validated in the context of the PhysioNet Computing in Cardiology Challenge 2017, thus reporting a global F 1 measure of 0.73 for the training set and 0.71 for the testing group. Nonetheless, to evaluate the method in a common scenario for previous works, the widely used MIT-BIH AF database has also been considered. A notably higher F 1 score of 0.87 has been provided in this case. The significantly different balance between the number of AF, NSR and OR recordings in both databases could justify the obtained outcomes.

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“…Remote continuous monitoring has recently been shown to improve treatment outcomes [23] and to reduce time-totreatment for patients with atrial tachycardia burden [24], so it is important to develop algorithms that can monitor over longer periods of time and/or compute more advanced statistics that can better detect the arrhythmia burden.…”

Section: B Implantable Devicesmentioning

confidence: 99%

Real-Time Decision Policies With Predictable Performance

et al. 2018

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As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case performance, on the basis of which hardware can be designed and algorithms can be tuned. Abstract-As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case per...

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Early Detection and Treatment of Atrial Arrhythmias Alleviates the Arrhythmic Burden in Paced Patients: The SETAM Study

Abstract: Background: Remote monitoring (RM) can remotely detect atrial tachyarrhythmias (ATAs (PACE 2017; 40:527-536)

Cited by 41 publications

References 39 publications

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

Atrial Fibrillation Screening through Combined Timing Features of Short Single-Lead Electrocardiograms

Real-Time Decision Policies With Predictable Performance

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