Morphology‐Enhanced Atrial Event Classification Improves Sensing in Pacemakers

Lewalter, Thorsten; Tuininga, Ype S.; Fröhlig, Gerd; Remerie, Susann; Eberhardt, Frank; Schmidt, Jörg; Groeningen, Chris Van; Wohlgemuth, Peter

doi:10.1111/j.1540-8159.2007.00891.x

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…To discriminate FFRW from activations, clustering-based methods using amplitude and minimum derivative have been proposed (Lewalter et al 2007, Padeletti et al 2005, Van Hemel et al 2004. Although often appropriate, this approach was clearly failing for some of our patients.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Automatic detection and classification of human epicardial atrial unipolar electrograms

Dubé¹,

Vinet²,

Xiong³

et al. 2009

Physiol. Meas.

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This paper describes an unsupervised signal processing method applied to three-channel unipolar electrograms recorded from human atria. These were obtained by epicardial wires sutured on the right and left atria after coronary artery bypass surgery. Atrial (A) and ventricular (V) activations had to be detected and identified on each channel, and gathered across the channels when belonging to the same global event. The algorithm was developed and optimized on a training set of 19 recordings of 5 min. It was assessed on twenty-seven 2 h recordings taken just before the onset of a prolonged atrial fibrillation for a total of 1593697 activations that were validated and classified as normal atrial or ventricular activations (A, V) and premature atrial or ventricular activations (PAA, PVA). 99.93% of the activations were detected, and amongst these, 99.89% of the A and 99.75% of the V activations were correctly labelled. In the subset of the 39705 PAA, 99.83% were detected and 99.3% were correctly classified as A. The false positive rate was 0.37%. In conclusion, a reliable fully automatic detection and classification algorithm was developed that can detect and discriminate A and V activations from atrial recordings. It can provide the time series needed to develop a monitoring system aiming to identify dynamic predictors of forthcoming cardiac events such as postoperative atrial fibrillation.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Automatic detection and classification of human epicardial atrial unipolar electrograms

Dubé¹,

Vinet²,

Xiong³

et al. 2009

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…37,38 Finally, digital processing of incoming signals permits grouping of atrial, ventricular and FFRW events and promotes better discrimination between true P waves, atrial arrhythmias and inadvertent signals. After a short learning period, these PM algorithms can adapt continuously to new wave forms and discriminate between various types of atrial tachyarrhythmias.…”

Section: The Pacemakermentioning

confidence: 99%

“…After a short learning period, these PM algorithms can adapt continuously to new wave forms and discriminate between various types of atrial tachyarrhythmias. 37,38 Verifying automated detection of atrial tachycardia For the diagnosis of atrial signals, the PM utilises the electrogram (EMG) transmitted by the atrial lead. Atrial signals are modified by the atrial amplifier (see above).…”

Section: The Pacemakermentioning

confidence: 99%

Diagnostic tools for atrial tachyarrhythmias in implantable pacemakers: a review of technical options and pitfalls

Voogt¹,

Hemel²

2008

NHJL

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Not only pacing algorithms and diverse technical PM features but also the atrial lead configuration are currently the limiting factors to the fully reliable, automated detection and diagnosis of atrial tachyarrhythmias. If these technical shortcomings can be improved, better signal processing will result. Then atrial pacing to prevent or suppress atrial tachyarrhythmias will be more justified. (Neth Heart J 2008;16:201-10.).

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Low proportion of biventricular pacing in a cardiac resynchronization therapy pacemaker device: what is the mechanism?

Pilecky

Kröll

Doering

et al. 2022

Herzschr Elektrophys

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Morphology‐Enhanced Atrial Event Classification Improves Sensing in Pacemakers

Abstract: By improving atrial signal discrimination, morphology analysis of atrial electrograms allows for high atrial sensitivity settings, and potentially improves the reliability of atrial arrhythmia diagnostics in heart rhythm devices.

Cited by 8 publications

References 22 publications

Automatic detection and classification of human epicardial atrial unipolar electrograms

Automatic detection and classification of human epicardial atrial unipolar electrograms

Diagnostic tools for atrial tachyarrhythmias in implantable pacemakers: a review of technical options and pitfalls

Low proportion of biventricular pacing in a cardiac resynchronization therapy pacemaker device: what is the mechanism?

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