Morphology-Enhanced Atrial Event Classification Improves Sensing in Pacemakers.
BACKGROUND: In atrial-based pacing, appropriate therapy and reliable diagnostics depend on detection and discrimination of atrial signals. Accurate classification of atrial events is mainly confounded by oversensing of ventricular far-field R-wave signals (FFRW), but attempts to reject FFRWs by mani...
| Publicado en: | Pacing & Clinical Electrophysiology Vol. 30; no. 12; pp. 1455 - 1464 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2007
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105847743&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105847743 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01478389 4F8 jtl: Pacing & Clinical Electrophysiology issn: 01478389 maglogo: Y pubinfo: dt: Dec2007 vid: 30 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105847743 2009732788 10.1111/j.1540-8159.2007.00891.x NLM18070298 105847743 ppf: 1455 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Morphology-Enhanced Atrial Event Classification Improves Sensing in Pacemakers. aug: au: LEWALTER T TUININGA Y FRÖHLIG G REMERIE S EBERHARDT F SCHMIDT J VAN GROENINGEN C WOHLGEMUTH P affil: Department of Cardiology, University of Bonn, Bonn, Germany sug: subj: Cardiac Pacing, Artificial Methods Cardiac Pacing, Artificial Standards Heart Atrium Physiopathology Pacemaker, Artificial Standards Aged Algorithms Atrial Fibrillation Physiopathology Atrial Fibrillation Therapy Bundle-Branch Block Physiopathology Bundle-Branch Block Therapy Chi Square Test Electrocardiography, Ambulatory Female Male Nonparametric Statistics Perception Prospective Studies Sensitivity and Specificity Sick Sinus Syndrome Physiopathology Sick Sinus Syndrome Therapy Signal Processing, Computer Assisted Human Aged: 65+ years Female Male ab: BACKGROUND: In atrial-based pacing, appropriate therapy and reliable diagnostics depend on detection and discrimination of atrial signals. Accurate classification of atrial events is mainly confounded by oversensing of ventricular far-field R-wave signals (FFRW), but attempts to reject FFRWs by manipulating atrial sensitivity and/or postventricular atrial blanking period (PVAB) may result in undersensing (especially of atrial fibrillation, AF) or in 2:1 atrial flutter detection. The objective of this study is therefore to evaluate if such methods can be improved by morphology-enhanced atrial event classification (MORPH). METHODS: Twenty-four-hour ambulatory atrial electrograms were recorded from continuous telemetry of digital pacemakers. Half of the recording was used for collecting two individual morphology parameters that discriminated P-waves from FFRWs in every patient (learning phase). The other half was used to test the MORPH algorithm against traditional methods (classification phase). RESULTS: In 44/48 patients, data were suitable for analysis. Average P and FFRW amplitudes were 1.96 mV versus 0.61 mV (P < 0.001). The interval between ventricular events and FFRW oversensing (VA interval) averaged at 14 ms during sensing and at 118 ms during pacing in the ventricle. Compared to nominal ('Factory') settings, the MORPH algorithm improved the sensitivity for P-wave recognition from 97.2% to 99.2%, the specificity from 91.9% to 99.96%, and the accuracy from 95.3% to 99.4% (P < 0.01 for all). CONCLUSIONS: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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